In computer vision, the partitioning of weighted graphs has been successfully applied to such tasks as image segmentation, object tracking and pose estimation. Most graph clustering methods work with positive edge weights only, which can be interpreted as similarities or distances between the nodes. These methods are parameter-based and require users to specify the desired numbers of clusters or a termination criterion (e.g. spectral clustering or iterated normalized cuts) or even to add a seed for each object (e.g. seeded watershed or random walker).
Other graph clustering methods work with so-called signed graphs, which include both positive and negative edge weights corresponding to attraction and repulsion between nodes. The advantage of signed graphs over positive-weighted graphs is that balancing attraction and repulsion allows us to perform the clustering without defining additional parameters. This can be done optimally by solving the so-called multicut optimization or correlation clustering problem kappes2011globally ; chopra1991multiway . This problem is NP-hard, but approximate solvers have already been proposed beier2016efficient . Besides, the general problem of graph partitioning can be solved approximately by greedy agglomerative clustering keuper2015efficient ; levinkov2017comparative ; wolf2018mutex ; kardoostsolving .
Agglomerative clustering algorithms for signed graphs have clear advantages: they are parameter-free and efficient. Despite the fact that there exists a variety of these algorithms, no overarching study has so far been made to compare their robustness and efficiency or to provide guidelines for matching an algorithm to the partitioning problem at hand.
In this paper, we propose a novel theoretical framework that generalizes over agglomerative algorithms for signed graphs by linking them to hierarchical agglomerative clustering on positive-weighted graphs lance1967general . This framework defines an underlying basic algorithm and allows us to explore its combinations with different linkage criteria and cannot-link constraints. We then formally prove that some of the combinations correspond to existing clustering algorithms and introduce new algorithms for combinations which have not been explored yet.
We evaluate and compare these algorithms on instance segmentation - a computer vision task of assigning each pixel of an image to an object instance. We use a CNN to predict the edge weights of a graph such that each node represents a pixel of the image, similarly to liu2018affinity ; lee2017superhuman ; wolf2018mutex , and provide these weights as input to the algorithms in our framework (see Fig. 1).
With our comparison experiments, performed both on 2D urban scenes from the CityScapes dataset and 3D electron microscopy image volumes of neurons, we evaluate the properties of the algorithms in our framework, focusing on their efficiency, robustness and tendency to over- or under-cluster. We show that one of the new algorithms derived from our framework, based on an average linkage criterion, outperforms the previously known agglomeration methods expressed in the framework. It also achieves competitive performance on the challenging CREMI 2016 segmentation benchmark and represents the best-performing proposal-free method on CityScapes. Our code is available athttps://github.com/abailoni/GASP.
2 Related work
Proposal-based methods have been highly successful in instance segmentation competitions like MS COCO lin2014microsoft , Pascal VOC2012 everingham2010pascal and CityScapes cordts2016cityscapes . They decompose the instance segmentation task into two steps that consists in generating object proposals and assigning to each bounding box a class and a binary segmentation mask he2017mask ; liu2018path ; yang2012layered ; li2017fully ; ladicky2010and ; hariharan2014simultaneous ; chen2015multi ; dai2016instance ; liang2016reversible . They commonly rely on Faster-RCNN ren2015faster and can be trained end-to-end using non-maximum suppression. Other methods use instead recurrent models to sequentially generate instances one-by-one romera2016recurrent ; ren2017end .
Proposal-free methods adopt a bottom-up approach by directly grouping pixels into instances. Recently, there has been a growing interest for such methods that do not involve object detection, since, in certain types of data, object instances cannot be approximated by bounding boxes. For example, the approach proposed in kirillov2017instancecut uses a combinatorial framework for instance segmentation; SGN liu2017sgn sequentially group pixels into lines and then instances; a watershed transform is learned in bai2017deep by also predicting its gradient direction, whereas the template matching uhrig2016pixel deploys scene depth information. Others use metric learning to predict high-dimensional associative pixel embeddings that map pixels of the same instance close to each other, while mapping pixels belonging to different instances further apart fathi2017semantic ; newell2017associative ; de2017semantic ; kulikov2018instance . Final instances are then retrieved by applying a clustering algorithm, like in the end-to-end trainable mean-shift pipeline of kong2018recurrentPix .
also experienced recent progress thanks to deep learning, both on natural imagesxie2015holistically ; kokkinos2015pushing and biological data lee2017superhuman ; schmidt2018cell ; meirovitch2016multi ; ciresan2012deep . In neuron segmentation for connectomics, a field of neuroscience we also address in our experiments, boundaries are converted to final instances with subsequent postprocessing and superpixel-merging: some use loopy graphs kaynig2015large ; krasowski2015improving or trees meirovitch2016multi ; liu2016sshmt ; liu2014modular ; funke2015learning ; uzunbas2016efficient to represent the region merging hierarchy; the lifted multicut beier2017multicut formulates the problem in a combinatorial framework, while flood-filling networks januszewski2018high eliminate superpixels by training a recurrent CNN to perform region growing one region at the time. A structured learning approach was also proposed in funke2018large ; turaga2009maximin .
Agglomerative graph clustering has often been applied to instance segmentation ren2013image ; liu2016image ; salembier2000binary , because of its efficiency as compared to other top-down approaches like graph cuts. Novel termination criteria and merging strategies have often been proposed: the agglomeration in malmberg2011generalized deploys fixed sets of merge constraints; ultrametric contour maps arbelaez2011contour combine an oriented watershed transform with an edge detector, so that superpixels are merged until the ultrametric distance exceeds a learned threshold; the popular graph-based method felzenszwalb2004efficient stops the agglomeration when the merge costs exceed a measure of quality for the current clusters. The optimization approach in kiran2014global performs greedy merge decisions that minimize a certain energy, while other pipelines use classical HAC linkage criteria, e.g. average linkage liu2018affinity ; lee2017superhuman , median funke2018largenunez2013machine ; knowles2016rhoananet .
Clustering of signed graphs
has the goal of partitioning a graph with both attractive and repulsive cues. Finding an optimally balanced partitioning has a long history in combinatorial optimizationgrotschel1989cutting ; grotschel1990facets ; chopra1993partition . NP-hardness of the correlation clustering problem was shown in bansal2004correlation , while the connection with graph multicuts was made by demaine2006correlation
. Modern integer linear programming solvers can tackle problems of considerable sizeandres2012globally , but accurate approximations pape2017solving ; beier2016efficient ; yarkony2012fast , greedy agglomerative algorithms levinkov2017comparative ; wolf2019mutex ; keuper2015efficient ; kardoostsolving and persistence criteria lange2018partial ; lange2018combinatorial have been proposed for even larger graphs.
3 Generalized framework for agglomerative clustering of signed graphs
In this section, we first define notation and then introduce one of our main contributions: a signed graph partitioning algorithm (Sec. 3.2) that can be seen as a generalization of several existing and new clustering algorithms (Sec. 3.3).
3.1 Notation and graph formalism
We consider an undirected simple edge-weighted graph with both attractive and repulsive edge attributes. In computer vision applications, the nodes can represent either pixels, superpixels or voxels. We call the set a clustering or partitioning with clusters if , for different clusters and every cluster induces a connected subgraph of . We also denote as the cluster associated with node . The weight function associates to every edge a positive scalar attribute representing a merge affinity or a similarity measure: the higher this number, the higher the inclination of the two incident vertices to be assigned to the same cluster111Note that other formalisms for positively weighted graphs associate distances to the edges, thus, the lower the edge weight, the higher the attraction between the two linked nodes, contrary to our definition of .. On the other hand, associates to each edge a split tendency : the higher this weight, the more the incident vertices would like to be in different clusters. Graphs of the type are also often defined as signed graphs , featuring positive and negative edge weights . Following the theoretical considerations in lange2018partial , we define these signed weights as . Some approaches directly compute , whereas others compute and separately. In this formalism, graphs with purely attractive interactions are a special case of with .
Inter-cluster interaction We call two clusters adjacent if there exists at least one edge connecting a node to a node . In hierarchical agglomerative clustering, the interaction between the two clusters is usually defined as a function , named linkage criterion, depending on the weights of all edges connecting clusters and , i.e. . All the linkage criteria tested in this article are listed and defined in Table 1.
3.2 GASP: generalized algorithm for signed graph partitioning
In Algorithm 1, we provide simplified pseudo-code for the proposed GASP algorithm. GASP implements a bottom-up approach that starts by assigning each node to its own cluster and then iteratively merges pairs of adjacent clusters. The algorithm has two variants. The first one, with addCannotLinkConstraints=False, starts by merging clusters with the strongest attractive interaction and stops once the remaining clusters share only mutual repulsive interactions (see iterations on toy graphs in block 4 of Fig. 1). After each merging iteration, the interaction between the merged cluster and its neighbors is updated according to one of the linkage criteria listed in Table 1.
In the second variant, when addCannotLinkConstraints=True, Algorithm 1 also introduces cannot-link constraints, which represent mutual exclusion relationships between pairs of nodes that cannot be associated with the same cluster in the final clustering. This variant selects the pair of clusters with the highest absolute interaction , so that the most attractive and the most repulsive pairs are analyzed first (see example in Fig. 1(b)). If the interaction is repulsive, then the two clusters are constrained and its members can never merge in subsequent steps. If the interaction is attractive, then the clusters are merged, provided that they were not previously constrained. The algorithm terminates when all the remaining clusters are constrained.
|GASP linkage criteria||Unsigned Graphs||Signed Graphs|
|No Constraints||With Constraints|
|Sum:||Sum Linkage Hier. Aggl. Clust.||GAEC keuper2015efficient||Greedy Fixation levinkov2017comparative|
|Absolute Max:||with||Single Linkage Hier. Aggl. Clust.||Mutex Watershed wolf2018mutex||Mutex Watershed wolf2018mutex|
|Average:||Average Linkage Hier. Aggl. Clust.||NEW||NEW|
|Max:||Single Linkage Hier. Aggl. Clust.||NEW||NEW|
|Min:||Complete Linkage Hier. Aggl. Clust.||NEW||NEW|
3.3 GASP with different linkage criteria: new and existing algorithms
Our main contribution is the generalized algorithm for signed graph partitioning, short GASP, that encompasses several known and novel agglomerative algorithms on display in Table 1. In our framework, individual algorithms are differentiated by the linkage criterion employed. We review them in the following paragraphs.
In the special case of an unsigned graph with only positive interactions, i.e. and
, the algorithm performs a standard agglomerative hierarchical clustering by returning only a single cluster and a hierarchy of clusters defined by the order in which the clusters are merged (see Table1, unsigned graphs).
Given a graph with both attractive and repulsive cues, an edge contraction algorithm with a sum update rule was pioneered in levinkov2017comparative ; keuper2015efficient (Table 1, Sum linkage). The authors present both a version with cannot-link constraints and one without, and then compare them with other greedy local-search algorithms approximating the multicut optimization problem. The Mutex Watershed wolf2018mutex is another signed graph partitioning algorithm that introduces dynamical cannot-link constraints. In Proposition 7.1 (see Appendix 7.2) we prove that, surprisingly, it can also be seen as an efficient implementation of GASP with Absolute maximum linkage (def. in Table 1). Moreover, in Proposition 7.2 we also prove that GASP with Abs Max linkage returns the same clustering with or without enforcing cannot-link constraints. On the other hand, to our knowledge, Average, Max or Min linkage criteria have never been used for signed graph agglomerative algorithms or been combined with cannot-link constraints.
Apart from the linkage criteria defined in Table 1, additional ones were proposed in the literature: nunez2013machine for example uses a learned approach where a random forest classifier updates the cluster interactions depending on predefined edge and node features; other approaches introduce a weight regularization depending on the size of the clusters felzenszwalb2004efficient ; kardoostsolving , whereas funke2018large uses a quantile linkage criteria by populating a histogram for each inter-cluster interaction. In our experiments, we decided to focus on the linkage criteria listed in Table 1, since they represent the most common options.
4 Experiments on neuron segmentation
We first evaluate and compare the agglomerative clustering algorithms described in the generalized framework on the task of neuron segmentation in electron microscopy (EM) image volumes. This application is of key interest in connectomics, a field of neuro-science with the goal of reconstructing neural wiring diagrams spanning complete central nervous systems. Currently, only proof-reading or manual tracing yields sufficient accuracy for correct circuit reconstruction schlegel2017learning , thus further progress is required in automated reconstruction methods.
EM segmentation is commonly performed by first predicting boundary pixels beier2017multicut ; ciresan2012deep or undirected affinities wolf2018mutex ; lee2017superhuman ; funke2018large , which represent how likely it is for a pair of pixels to belong to the same neuron segment. The affinities do not have to be limited to immediately adjacent pixels. Thus, similarly to lee2017superhuman , we train a CNN to predict both short- and long-range affinities and use them as edge weights of a 3D grid graph, where each node represents a pixel/voxel of the volume image.
4.1 Data: CREMI challenge
We evaluate all algorithms in the proposed framework on the competitive CREMI 2016 EM Segmentation Challenge cremiChallenge that is currently the neuron segmentation challenge with the largest amount of training data available. The dataset comes from serial section EM of Drosophila fruit-fly tissue and consists of 6 volumes of 1250x1250x125 voxels at resolution 4x4x40nm, three of which present publicly available training ground truth. The results submitted to the leaderboard are evaluated using the CREMI score (https://cremi.org/leaderboard/), based on the Adapted Rand-Score (Rand-Score) and the Variation of Information Score arganda2015crowdsourcing . In Appendix 7.4, we provide more details about the training of our CNN model, inspired by work of lee2017superhuman ; funke2018large .
4.2 Results and discussion
Comparison of linkage criteria Table 2 shows how the agglomerative algorithms derived from our framework compare to each other. For a simple baseline, we also include a segmentation produced by thresholding the affinity predictions (THRESH). GASP with Average linkage, representing one of the new algorithms derived from our generalized framework, significantly outperformed all other previously proposed agglomerative methods like GAEC (GASP Sum) keuper2015efficient , Greedy Fixation (GASP Sum + Constraints) levinkov2017comparative or Mutex Watershed (GASP Abs. Max.) wolf2018mutex . The competitive performance of this simple parameter-free algorithm is also reported in Table 3, showing the current leader-board of the challenge: all entries, apart from GASP, employ superpixel-based post-processing pipelines, several of which rely on the lifted multicut formulation of beier2017multicut that uses several random forests to predict graph edge weights, relying not only on information derived from affinity maps but also raw data and shape information. Note that the test volumes contain several imaging artifacts that make segmentation particularly challenging and might profit from more robust edge statistics of super-pixel based approaches. On the other hand, the fact that our algorithm can operate on pixels directly removes the parameter tuning necessary to obtain good super-pixels and can also avoid errors that result from wrong superpixels that cannot be fixed during later agglomeration. In Appendix 7.5, we provide more details about how we scaled up GASP to the full datasets. Appendix Table 5 lists the performances and the run-times for all tested GASP linkage.
for more details). For each experiment, some of the long-range CNN predictions were randomly selected with probabilityand added as long-range edges to the pixel grid-graph. Experiments are performed on a crop of CREMI training sample B.
Noise experiments Additionally, we conduct a set of experiments where the CNN predictions are perturbed by structured noise, in order to highlight the properties of each GASP variant and perform an in-depth comparison that is as quantitative as possible. Appendix 7.6 introduces the type of spatially correlated noise that allowed us to perturb the CNN outputs by introducing simulated additional artifacts like missing or false positive boundary evidence. Fig. 4 summarizes our 12000 noise experiments: we focus on the best performing linkage criteria, i.e. Average, Sum and Abs Max, and test them with different amount of noise. In these experiments, we also want to assess how beneficial it is to use long-range CNN predictions in the agglomeration. Thus, we perform a set of simulations without adding long-range connections to the grid-graph and another set where we introduce them with a 10% probability222We also performed experiments adding all the long-range predictions given by the CNN model, but we did not note major differences when using only 10% of them. Adding this fraction is usually sufficient to improve the scores..
Average and Abs Max linkage Our findings confirm that GASP with Average linkage criterion represents the most robust algorithm tested and the one that benefits the most from using the long-range CNN predictions. On the other hand, it is not a surprise that the Abs Max statistic proposed by wolf2018mutex is less robust to noise than the Average linkage, but, as we show in the Appendix Table 5, Abs Max represents a valid and considerably faster option. Adding long-range connections to the graph is generally helpful, but when many of them carry repulsive weights, then GASP with cannot-link constraints shows a clear tendency to over-cluster.
Sum linkage All our experiments show that GASP with Sum linkage is the algorithm with the highest tendency to under-cluster and incorrectly merge segments (see Fig. 3 for an example). This property is related to the empirical observation that a Sum statistic tends to grow clusters one after the other, as shown in Fig. 1 by the quite unique agglomeration order of the Sum statistic. An intuitive explanation of this fact is the following: initially, most of the intra-cluster nodes present similar attractive interactions between each others; when the two nodes sharing the most attractive interaction are merged, there is a high chance that they both share an attractive interaction with a common neighboring node, so the new interaction with this common neighbor will be immediately assigned to a high priority in the agglomeration, given by the sum of two high weights; this usually starts a “chain reaction”, where only a single cluster is agglomerated at the beginning. On the other hand, as we also see in Fig. 1, other linkage criteria like Average or Abs Max grow clusters of similar sizes in parallel and accumulate in this way much more reliable inter-cluster statistics.
5 Experiments on CityScapes
We also evaluate the performances of GASP on the CityScapes dataset cordts2016cityscapes , which consists of 5000 street-scene images: 2975 for training, 500 for validation and 1525 for testing. We used the pipeline proposed in GMIS liu2018affinity , representing the proposal-free method performing best on the dataset. The pipeline consists of two CNN with similar structures, one predicting pixel level semantic scores and the other predicting pixel affinities between instances. The code and the model are publicly available, so we provided its output affinities as input to GASP. In Appendix 7.7 we present how we fine-tuned the model by using a Søresen-Dice loss, similarly to wolf2018mutex .
Results are summarized in Table 4 and Fig. 5: the best scores are achieved by PANet liu2018path , which is a proposal-based method strongly related to Mask R-CNN. GASP with Average linkage achieves competitive results and outperforms all previously proposed proposal-free methods. Similarly to the experiments on neuron segmentation, other linkage criteria tend to over-cluster, like Abs Max, or under-cluster and merge instances, like Sum. The graph-merging algorithm proposed by liu2018affinity (MultiStepHAC) requires the user to tune several threshold parameters and when we applied it to the affinities predicted by our fine-tuned model it achieved an AP score of 33.0 on the validation set, which is worse than the original value 34.1 reported in liu2018affinity . This is probably due to the fact that the agglomeration MultiStepHAC was tailored to the output affinities of the original model. Table 6 in Appendix includes the scores of all other tested GASP algorithms.
We have presented a novel unifying framework for agglomerative clustering of graphs with both positive and negative edge weights and we have shown that several existing clustering algorithms, e.g. the Mutex Watershed wolf2018mutex , can be reformulated as special cases of one underlying agglomerative algorithm. This framework also allowed us to introduce new algorithms, one of which, based on an Average linkage criterion, outperformed all the others: It proved to be a simple and remarkably robust approach to process short- and long-range predictions of a CNN applied to an instance segmentation task. On biological images, this simple average agglomeration algorithm can represent a valuable choice for user who is not willing to spend much time tuning complex task-dependent pipelines based on superpixels. In future work we plan to extend the comparison to other types of non-image graphs and explore common theoretical properties of the algorithms included in the framework.
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7 Supplementary material
7.1 Implementation details and complexity of GASP
During the agglomerative process, the interaction between adjacent clusters has to be properly updated and recomputed, as shown in Algorithm 1. An efficient way of implementing these updates can be achieved by representing the agglomeration as a sequence of edge contractions in the graph. Given a graph and a clustering , we define the associated contracted graph , such that there exists exactly one representative for every cluster . Edges in represent adjacency-relationships between clusters and the signed edge weights are given by inter-cluster interactions . For the linkage criteria tested in this work, when two clusters and are merged, the interactions between the new cluster and each of its neighbors depend only on the previous interactions involving and . Thus, we can recompute these interactions by using an update rule that does not involve any loop over the edges of the original graph :
As we show in Algorithm 2, our implementation of GASP is based on an union-find data structure and a heap allowing deletion of its elements. The algorithm starts with each node assigned to its own cluster and sorts all edges in a heap/priority queue (PQ) by their absolute weight in descending order, so that the most attractive and the most repulsive interactions are processed first. It then iteratively pops one edge from PQ and, depending on the priority , does the following: in case of attractive interaction , provided that was not flagged as a cannot-link constraint, then merge the connected clusters, perform an edge contraction of in and update the priorities of new double edges as explained in Fig. 6. If, on the other hand, the interaction is repulsive () and the option addCannotLinkContraints of Alg. 2 is True, then the edge is flagged as cannot-link constraint.
In the main loop, the algorithm iterates over all edges, but the only iterations presenting a complexity different from are the ones involving a merge of two clusters, which are at most . By using a union-find data structure (with path compression and union by rank) the time complexity of merge and find() operations is , where is the slowly growing inverse Ackerman function. The algorithm then iterates over the neighbors of the merged cluster (at most ) and updates/deletes values in the priority queue (). Therefore, similarly to a heap-based implementation of hierarchical agglomerative clustering, our implementation of GASP has a complexity of . In the worst case, when the graph is dense and , the algorithm requires memory. Nevertheless, in our practical applications the graph is much sparser, so . With a single-linkage, corresponding to the choice of the Maximum update rule in our framework, the algorithm can be clearly implemented by using the more efficient Kruskal’s Minimum Spanning Tree algorithm with complexity . Moreover, in the next section, we present an efficient implementation of GASP with Absolute Maximum linkage that has empirical complexity.
7.2 Properties of GASP with Absolute Maximum linkage
Remark on graph notation
The definition of a graph proposed by wolf2018mutex makes a distinction between a set of positive edges , associated with a set of positive scalar attributes representing merge affinities, and a set of negative edges , associated with a set of positive attributes representing split tendencies. On the other hand, in our definition each edge have both an attractive and a repulsive attribute, so we can make them equivalent by defining:
Both algorithms sort edges in descending order of the absolute interactions and then iterate over all of them. The only difference is that MWS, after merging two clusters, does not update the interactions between the new cluster and its neighbors. However, since with an Abs. Max. linkage the interaction between clusters is simply given by the edge with highest absolute weight , the order by which edges are iterated over in GASP is never updated. Thus, both algorithms perform precisely the same steps and return the same clustering. ∎
In the GASP Algorithm 2, the clustering is updated only when two clusters are merged and the condition at line 9 is satisfied. We also observe that, in the unconstrained version of GASP, the predicate canBeMerged at line 9 can never be false because cannot-link constraints are never introduced at line 14. Let us now contradict the initial hypothesis and assume by absurd that the constrained version of GASP introduces a cannot-link constraints between two clusters sharing a positive interaction and outputs a different clustering as compared to the unconstrained version. This can happen only in the situation shown in Fig. 7, when two clusters and are merged together and share a common neighboring node having the following two properties: a) and are already constrained and share a repulsive interaction , b) and share an attractive interaction that is higher in absolute value . Then, according to Eq. 4, the new merged cluster and are constrained and share a positive interaction. But this case can never happen, since if then clusters and are merged before clusters and are constrained. ∎
7.3 Predicting signed edge weights with a CNN
Our CNN model outputs affinities in the form of pseudo-probabilities , where represents a boundary evidence. In order to use them as input of the algorithms in our framework, we mapped them to positive and negative values333Note that in general attractive and repulsive interactions and can be independently estimated with different classifiers.. The most common approaches use additive ailon2008aggregating or logarithmic finkel2008enforcing ; andres2012globally mappings:
where is a bias parameter that allow a tuning between over- and under-segmentation. We evaluated both of them empirically with each of the tested linkage and found that the additive mapping is the best option in all cases apart from the Sum linkage. Note that varying the parameter does not usually define a hierarchy of nested clusterings, thus it is not equivalent to varying a threshold parameter in HAC. This hierarchical property is only valid for GASP without constraints and with Average, Max or Min linkage.
7.4 Neuron segmentation and compared methods
The data from the CREMI challenge is highly anisotropic and contains artifacts like missing sections, staining precipitations and support film folds. To alleviate difficulties stemming from misalignment, we use a version of the data that was elastically realigned by the challenge organizers with the method of saalfeld2012elastic . We train a 3D U-Net ronneberger2015u ; cciccek20163d using the same architecture as funke2018large and predict long-and-short range affinities as described in lee2017superhuman . In addition to the standard data augmentation techniques of random rotations, random flips and elastic deformations, we simulate data artifacts. In more detail, we randomly zero-out slices, decrease the contrast of slices, simulate tears, introduce alignment jitter and paste artifacts extracted from the training data. Both funke2018large and lee2017superhuman have shown that these kinds of augmentations can help to alleviate issues caused by EM-imaging artifacts. We use L2 loss and Adam optimizer to train the network. The model was trained on all the three samples with available ground truth labels.
THRESH and WSDT
The basic post-processing methods we consider cannot take long-range affinities into account, so we only consider direct neighbors affinities and generate a boundary map by taking an average over the 3 directions. Based on this boundary map, we run connected components (THRESH) and we also run a watershed algorithm seeded at the maxima of the smoothed distance transform (WSDT). For WSDT, the degree of smoothing was optimized such that each region receives as few seeds as possible, without however causing severe under-segmentation. Due to the anisotropy of the data, we generate 2D WSDT superpixels by considering each 2D image in the stack singularly.
Given the 2D WSDT superpixels, we build a 3D region-adjacency graph such that each node represents a superpixel. The weights of the edges connecting neighboring superpixels are computed by taking an average over both short- and long-range affinities connecting the two regions. We then convert the edge probabilities to signed weights using the logarithmic mapping defined in Eq. 5 and solve the multicut problem on this graph. For our experiments, we use the approximate Kernighan-Lin solver keuper2015efficient ; kernighan1970efficient (WSDT+MC). In some cases, the long-range affinities predicted by the CNN can connect two superpixels that are not direct-neighbors. Thus, in these cases we introduce additional lifted edges in the graph and an instance of the lifted multicut problem (WSDT+LMC). This time, similarly to the methods mentioned in beier2016efficient , we used a combination of approximate solvers consisting in GAEC and Kernighan-Lin.
7.5 GASP on the full CREMI dataset
For the predictions on the full dataset from the CREMI challenge, we used the padded volumes provided by the challenge. The crops on which we performed a prediction have a size ofvoxels or larger. Building a graph with nodes can easily incur a large use of memory, so we decided to perform a preprocessing step by initially merging some nodes together. Simply down-sampling the predictions of the CNN would have led to a loss of resolution and performances in the most difficult parts of the dataset. Thus, we decided to pre-merge the most connected components of the graph that would be anyway clustered during the first iterations of GASP. To do this, we used a simple approach: we generated a boundary probability map by taking for each voxel an average over affinities in all directions (both short- and long-range ones) and we run THRESH with a conservative threshold parameter to find the connected components. With this approach, pixels are pre-clustered only when they are far away (in all directions) from predicted boundaries. To make sure that in this preprocessing step different neurons are never merged together by mistake, we intersected the segments given by THRESH with the segments given by WSDT. We tested GASP both on the full grid-graph and on this preprocessed graph and we did not notice any major differences in the final clustering or achieved scores, although the version with a pre-processed graph was significantly faster. To reduce the runtime and memory requirements even further, we used only 10 % of the long-range connections in the pixel-graph, since adding all of them did not improve the scores.
Removing small segments
After running GASP, we use a simple post-processing step to delete small segments on the boundaries, most of which are given by single-voxel clusters. On the neuron segmentation predictions, we deleted all regions with less than 200 voxels and used a seeded watershed algorithm to expand the bigger segments.
Enforcing local merge
In 2D images of urban-scenes, due to partial occlusion, one object instance can be given by multiple components that are not directly connected in the image plane. This is not the case in neuron-segmentation, where each neuron should be given by a single 3D connected component in the volume. In order to enforce it, we modified the implementation of GASP so that two clusters are merged only when they represent two adjacent supervoxels in the 3D volume and if this condition is not satisfied, the merge is postponed until there is a direct connection. This then avoids the introduction of “air-bridges” between segments due to attractive long-range connections in the initial voxel grid-graph. This approach achieved superior performances to the one proposed in wolf2018mutex , where all long-range connections in the grid-graph are associated to a negative repulsive edge weight.
|GASP linkage||CREMI-Score (higher better)||Rand-Score (higher better)||VI-merge (lower better)||VI-split (lower better)||Runtime (lower better)|
|Sum + CLC levinkov2017comparative||0.282||0.906||0.358||0.510||4.64 10|
|Abs Max wolf2018mutex||0.322||0.897||0.286||0.735||1.24 10|
|Max + CLC||0.324||0.893||0.292||0.698||6.31 10|
|Sum keuper2015efficient||0.334||0.872||0.461||0.444||4.74 10|
|Average + CLC||0.563||0.772||0.259||1.142||2.95 10|
|Min + CLC||2.522||0.030||0.197||6.365||4.77 10|
7.6 GASP sensitivity to noise: adding artifacts to CNN predictions
Additionally to the comparison on the full training dataset, we performed more experiments on a crop of the more challenging CREMI training sample B, where we perturbed the predictions of the CNN with noise and we introduced additional artifacts like missing or fictitious boundary evidences.
In the field of image processing there are several ways of adding noise to an image, among which the most common are Gaussian noise or Poisson shot noise. In these cases, the noise of one pixel does not correlate with its neighboring noise values. On the other hand, predictions of a CNN are known to be spatially correlated. Thus, we used Perlin noise444In our experiments, we used an open-source implementation of simplex noise perlin2001noise , which is an improved version of Perlin noise perlin1985image , one of the most common gradient noises used in procedural pattern generation. This type of noise generates spatial random patterns that are locally smooth but have large and diverse variations on bigger scales. We then combined it with the CNN predictions in the two following ways:
where ; and is a positive factor representing the amount of added noise. represents then a under-clustering biased prediction, such that the probability for two pixels to be in the same cluster is increased only if (see Fig. 8b), whereas is a over-clustering biased prediction with decreased probabilities when (Fig. 8c). In the implementation we used, the noise can be generated in an arbitrary number of dimensions and a smoothing factor can be specified for each direction independently. In our experiments, each pixel is represented by a node in the grid-graph and it is linked to other nodes by short- and long-range edges. Thus, the output of our CNN model has channels: for each pixel / voxel, it outputs values representing the weights of different edge connections. We then generated a 4-dimensional noise that matches the dimension of the CNN output. The data is highly anisotropic, i.e. it has a lower resolution in one of the dimensions. Due to this fact, we chose different smoothing parameters to generate the noise in different directions.
The experiments summarized in Fig. 4 were performed in the following way: for each value , 30 random noise samples were drawn, from which median and percentiles statistics were computed for each different linkage criteria. For each sample, we randomly selected some of the long-range predictions from the CNN and added them to pixel grid-graph.
7.7 Fine-tuning the GMIS pipeline on CityScapes
For our experiments, we used the model from GMIS liu2018affinity that is publicly available. The model consists of two neural networks with similar structures, one predicting pixel level semantic scores and the other predicting pixel affinities between instances. We also used all the affinity post-processing methods proposed in liu2018affinity , e.g. excluding background, resizing regions of interest or the proposed "affinity-refinement" method, which combines semantic and instance outputs. The instance-branch of the model was trained with a Binary Cross-Entropy loss, but we noticed how the short-range affinities were biased towards high probabilities, so that a strong short-range boundary evidence was never predicted by the model. In liu2018affinity , they handle this problem by proposing a modified version of HAC that is done in stages (MultiStepHAC): initially only short-range affinities are used to run HAC and a low threshold in the hierarchy is chosen to define a first clustering; then a new HAC problem including long-range affinities is initialized with the first clustering; in the method proposed by liu2018affinity , these steps are repeated three times.
Since MultiStepHAC is a rather complex post-processing method that requires to tune several hyper-parameters, we opted for a different approach to solve the problem of the unbalanced affinities. We added two 1x1 convolutional layers to the instance-branch model and trained them by using the same loss used by wolf2018mutex and is based on the Søresen-Dice coefficient dice1945measures ; sorensen1948method . Compared to Hamming-distance based loss like Binary Cross-Entropy or Mean Squared Error, the advantage of this loss is its being robust against prediction and / or target sparsity, that is a desirable quality in this application since boundaries between instances can be sparse. During training, all the affinities involving at least one pixel belonging to the background were ignored in the loss. In this way, these last two layers specialized in improving the predictions of boundary evidence between adjacent instances (especially those belonging to the same class). We then considered an average of these new fine-tuned affinities with the ones predicted by the original model. During the fine-tuning process, only the parameters of the last two convolutional layers were updated.
Before to apply GASP, we performed a parameter-search for the bias defined in 5. Table 6 lists the best-case performances for each of the methods: note that depending on the GASP linkage criterion, it was necessary to bias more or less the predicted edge weights.
The semantic categories are assigned to each instance in the same way proposed by liu2018affinity , i.e. with a majority vote based on the semantic output of the model.