Neuropsychological studies of brain lesion-deficit relationships are an indispensable means of determining what brain areas are critical for carrying out particular functions. This contrasts with functional brain imaging techniques such as functional magnetic resonance imaging (fMRI), which while extremely popular and useful, cannot make strong claims about what brain areas are necessary for the functions being investigated. A major impediment to progress in brain lesion-deficit studies, however, is the labor-intensive and ultimately subjective step of having an expert manually segment brain lesions from MRI scans.
This has been highlighted in previous studies comparing inter-rater variability and speed of human compared to automatic lesion identification. Fiez et al. fiez2000 report a 67% ( 7%) agreement in overlapping voxels between two expert raters across ten subjects. More recently, other groups have reported an inter-rater overlap of 0.73 0.2 between experts performing manual lesion segmentation for the ATLAS database liew2018large . When brain lesion segmentation is performed exclusively by experienced neuroradiologists, median inter-rater agreement has been shown to be as high as 0.78 neumann2009interrater . However, the involvement of only a small number of patients (N = 14) and the use of lower-resolution scans (6.5 mm slices rather than the typical 1 mm slices used in research) suggests that an inter-rater agreement of 0.78 may be inflated relative to the 0.67 to 0.73 range that seems typical for research studies.
Aside from concerns with inter-rater reliability, manually segmenting lesions is also time consuming, often taking between 4.8 to 9.6 hours. Methods developed for automating this process, however, can segment lesions in roughly a minute WILKE20112038 . However, manual lesion segmentation remains the method of choice, presumably due to the relatively poor accuracy of available automated methods WILKE20112038 ; ito2018comparison . Clearly what is needed is a fast, automated method for brain lesion segmentation with a better accuracy than currently available methods.
The first convolutional UNet ronneberger2015u and subsequent models such as UResNet kamnitsas2017efficient take as input 2D slices of the MRI image in a single orientation. They predict the lesion for each slice separately and then combine the predictions into a volume. This approach has limited accuracy because it does not consider the other two planes in the image volume. Without some method, such as a post-processing mechanism, for considering views from other orientations, models such as this will be inherently limited by how well a lesion can be detected in a single orientation view. For example, a wide and flat lesion might be readily distinguishable from healthy tissue in an axial but not coronal view. Indeed, a lesion that is more visible in sagittal and coronal views than in the axial view is shown in Figure 9.
To address this limitation, CNN systems have been introduced that can accommodate multiple 2D slice orientations. The dual-path CNN, DeepMedic kamnitsas2017efficient , while not considering multiple 2D orientations, does have two pathways, one for high and one for low resolution slices. Lyksborg et al. lyksborg2015ensemble use a three path network, one for each of the canonical axial, sagittal and coronal views. Indeed, multi-path systems with up to eight different network paths have been explored previously de2015deep . Adding paths, however, comes with a cost of having to fit many additional parameters for each path. Fitting these additional parameters leads to an increased risk of over-fitting, as has been reported for multi-path systems bernal2018deep .
Multi-path systems must also combine the predictions from each path into a final output. One approach to combining path predictions is a simple majority vote. This was the approach used by Lyksborg et al. lyksborg2015ensemble . However, this approach risks ignoring important but less frequently represented information, as the outputs from different paths are combined into a final voxel prediction by a simple majority vote. Also, the goal of their network was to segment tumors, where the pathology may present a somewhat different problem than stroke. Indeed, in the current work we show that majority vote performs less well on stroke lesion segmentation than a more inclusive 3D convolutional approach to combining outputs across paths.
We address shortfalls in previous approaches by proposing a novel nine-path system, where each path contains a custom U-Net to accommodate multiple MRI modalities or views, depending on the use case. For example, having both T1 and FLAIR modalities could be useful for segmenting sub-acute strokes that have occurred within, say, the last 5 weeks. For more chronic strokes having occurred more than 6 months previous, multiple T1 views might be more useful than combining with FLAIR. This possibility is tested in Table 1 below. Our system considers three different normalizations of the images along each of the three axial, sagittal and coronal views. Our custom U-Net is weak on its own but powerful as a component of our multi-path system. This makes sense in the context of ensemble learning where weak learners can perform better in an ensemble freund1997decision . We also use a 3D convolutional kernel to merge 2D outputs from each path and show that it gives a better accuracy than majority vote. It is because of this combination of 2D and 3D approaches that we refer to our system as 2.5D.
Critically, we address the challenging issue of model over-fitting by performing a rigorous cross-study validation to evaluate accuracy of lesion identification across sites that differ in numerous ways such as scanner model, patient sample, and expert tracers. This is done by training a model on one set of patient MRIs and then testing the ability of those trained parameters to identify lesions in a separate validation (test) set. Cross-study validation gives a better estimate of the model’s true accuracy compared to cross-validation, where train and test samples are simply re-shuffled from the same datasetbernau2014cross .
Details of our model are provided below, followed by experimental results across three different datasets. We show that our system has significantly higher agreement with ground-truth segmentations by human experts compared to the recent CNN-based methods DeepMedic kamnitsas2017efficient , the original UNet ronneberger2015u , a residual UNet guerrero2018white , and two non-CNN based machine learning methods based on random forests pustina2016automated and naive Bayes griffis2016voxel .
2.1 Convolutional neural networks
Convolutional neural networks are the current state of the art in machine learning for image recognition lecun1998gradient ; krizhevsky2012imagenet , including for MRI bernal2018deep . They are typically composed of alternating layers for convolution and pooling, followed by a final flattened layer. A convolution layer is specified by a filter size and the number of filters in the layer. Briefly, the convolution layer performs a moving dot product against pixels given by a fixed filter of size (usually or
). The dot product is made non-linear by passing the output to an activation function such as a sigmoid or rectified linear unit (also called relu or hinge) function. Both are differentiable and thus fit into the standard gradient descent framework for optimizing neural networks during training. The output of applying aconvolution against a image is an image of size . In a CNN, the convolution layers just described are typically alternated with pooling layers. The pooling layers serve to reduce dimensionality, making it easier to train the network.
2.2 Convolutional U-network
After applying a series of convolutional filters, the final layer dimension is usually much smaller than that of the input images. For the current problem of determining whether a given pixel in the input image is part of a lesion, the output must be of the same dimension as the input. This dimensionality problem was initially solved by taking each pixel in the input image and a localized region around it as input to a convolutional neural network instead of the entire image ciresan2012deep .
A more powerful recent solution is the Convolutional U-Net (U-Net) ronneberger2015u . This has two main features that separate it from traditional CNNs: (a) deconvolution (upsampling) layers to increase image dimensionality, and (b) connections between convolution and deconvolution layers. Another popular U-Net method is the residual U-Net (also known as UResNet guerrero2018whitehochreiter1998vanishing ).
2.3 U-Net systems
Since the introduction of the original U-net, several systems have been proposed for analyzing MRI images. DeepMedic is a popular multi-path 3D CNN model that combines high and low resolutions of input images. Previous systems like Lyksborg et. al. lyksborg2015ensemble consider the three axial, sagittal, and coronal planes in a multi-path ensemble, but use a potentially limiting majority vote approach to combine outputs from each path. Multi-path systems can be challenging to train, as can be seen in the work of Brebisson and Montana de2015deep . There they train eight networks in parallel to capture various aspects of the input image but report overfitting due to large number of parameters.
Post processing is another important component of U-Net systems to reduce false positives. The post processing methods range from simple ones like connected components and clustering lai2015deep ; havaei2017brain to using 3D CNNs and conditional random fields kamnitsas2017efficient . The latter methods also end up accounting for temporal dependence between slices, resulting in a higher accuracy.
2.4 Our CNN system
We developed a modified U-network in a multi-path multi-modal system with a 3D convolutional kernel for post-processing shown in Figure 1. A 3D kernel is like a 2D one except that it has a third dimension that it convolves into as well, and thus it expects a 3D input. For example, in a 2D system kernels are typically whereas in a 3D kernel it would be . Details of our system are provided below, highlighting differences in our approach compared to previous ones.
2.4.2 Multiple paths
Our primary motivation for taking a multi-path approach is to optimize the ability of the model to identify brain lesions by capturing image information from all three angles as well as their normalizations. To return to the overview of our system shown in Figure 1(a), consider the three different normalizations of each of the three axial, sagittal, and coronal planes. For each plane we normalize (1) in the same plane, (2) across the third plane, and (3) both in the same plane first and then across the third, thus giving nine paths. These choices were motivated by our preliminary results not shown here and previous studies showing that different planes work best for different lesion locations lyksborg2015ensemble , and that the best method of normalization may differ depending on image view bernal2018deep .
2.4.3 Basic U-net
First we look at details of our basic U-net that makes up the system. Our U-net used in each path is inspired by the original U-net ronneberger2015u and a more recent one tseng2017joint that attains state of the art accuracies on the BRATS brain tumor MRI benchmark menze:hal-00935640 . The encoder portion of our U-net is shown in Figure 2(c). After each convolution we perform a
average pooling with stride 2 to halve the image dimension. Features from the encoder are passed to the decoder. However, since there are two encoders (one for the original T1-weighted image and the other for its flipped version), corresponding features are combined using the block shown in Figure2(e). Alternatively, the current network can be used with two different MRI modalities by substituting the T1 image and its flipped version with separate left hemisphere T1-weighted and Fluid-Attenuated Inversion Recovery (FLAIR) images.
From each encoder we obtain a prediction of a lesion (in the respective normalization and plane) that we merge with a 3D convolutional kernel tseng2017joint ; lai2015deep . We take the two feature maps each of dimension where 32 is the number of convolutional filters from the encoder layer and is the input size depending upon the encoder layer (see Figure 2(a)). Stacking refers to adding an extra dimension to make the input for the 3D kernel. The 3D kernel gives an output of which is ”squeezed” to remove the unnecessary dimension to give an output of to the decoder.
The fused features are then given to the decoder, which we add to the output of deconvolutional layers (briefly explained below), a process shown as a sign in Figure 2(c). The image dimensions are preserved because of the addition. The previous U-net that served as a starting point for our current effort tseng2017joint performed element-wise multiplication of fused features with deconvolved ones. However, this is unlikely to be useful for the current system. Our fused features and upsampled features have small values, so their product would even be smaller. This in turn would give a gradient with zero or near-zero values that would affect the training. Thus we prevent this by adding instead of multiplying fused and upsampled feature values.
Shown in blue in Figure 2(d) are the convolutional blocks used in our encoder and decoder. We use
convolutional blocks with a stride of 1 and padding of one extra layer in the input to make the output dimensions same as the input. The previous U-net that inspired our designtseng2017joint performed Relu activation before adding fused features. Here we perform Relu activation twice. In the context of the decoder, this means Relu activation is performed after adding fused features to upsampled ones. Performing Relu activation after addition rather than before has been shown to be more accurate for image classification he2016identity .
Deconvolutional blocks (also known as transposed or fractionally strided convolutions) are meant to increase the dimensionality of imagesdumoulin2016guide . The term transpose arises from the fact that a deconvolution is simply the product of the transpose of the convolution weight matrix with the output when the stride is 1. If the stride is more than one we insert zeros in between the input to obtain the correct transpose result (as well-explained in Dumoulin and Visin dumoulin2016guide ) We use deconvolutions with a stride of 2 that doubles the image dimensions in both axes.
The output of each of the nine paths in our system is a 2D mask showing the predicted location of the lesion in the same view as the input image, as in Figure 2
(a). The lesion prediction mask is binarized by rounding to 0 if the values in the mask are below 0.5, otherwise values are rounded up to 1. We stack each predicted lesion with the original input image and combine all slices to form avolume. Since we have nine paths this becomes of size . This is passed to our 3D CNN post-processor as described below.
In the post-processor shown in Figure 1(b), we have a main path containing 36 3D kernels each with 18 channels, or equivalently 36 3D kernels each of size . Following that, the second 3D CNN in the main path has 9 3D kernels each with 36 channels, and two final 3D CNNs each of dimensions with 9 channels.
2.4.5 Loss function
The final output from the post-processor has two channels each of dimensions
. The target lesion has the same dimensions but just one channel. The first channel in our output predicts the lesion and the second one predicts the complement of it. We convert the outputs of each channel into probabilities with softmaxalpaydin
and combined them into a modified Dice loss functionmilletari2016v ; wong20183d . For a single channel output the Dice loss is defined to be where
are the predicted softmax outputs of the channel, and is 1 if the voxel has a lesion and 0 otherwise. If we are predicting the complement of the lesion then the values of are flipped from 0 to 1 and 1 to 0. With our two channel output and our loss becomes where the latter is for the complement.
2.5 Imaging Data
We obtained high-resolution (1 mm) whole-brain MRI scans from 25 patients from the Kessler Foundation (KES), a neuro-rehabilitation facility in West Orange, New Jersey. We also obtained 20 high-resolution scans from the Medical College of Wisconsin (MCW). Data heterogeneity is important for widespread applicability of the model. To that end, we included data from a variety of time points: subacute ( 5 weeks post stroke) and chronic ( 3 months post stroke). Strokes of both hemorrhagic and ischemic etiology were included. The lesions visualized on the scans were hand-segmented by a trained human expert, as described for the KES scans in boukrina2015neurally and the MCW scans pillay2014cerebral ; binder2016surface . To move these scans into standard Montreal Neurological Institute (MNI) reference space fonov2011unbiased , we used the non-linear warping tool, 3dQwarp, from the AFNI software suite cox1996afni . The segmented lesion was used as an exclusion mask so that the lesioned territory would be excluded from the warping procedure. This prevents non-lesioned brain tissue from being distorted to fill in the lesioned area. This transformation was performed on the T1 images, resulting in skull-stripped output in MNI space. This calculated transformation for each participant was then applied to the FLAIR image (KES only) and hand-traced lesion mask.
We also obtained scans and stroke lesion masks from the public ATLAS database liew2018large and processed them as just described for the KES and MCW data. We selected images according to the following criteria to focus on cases with single lesions in the left hemisphere:
Session = t01 [T1 Scans only] LH_Cort + LH_SubCort = 1 [Cortical OR Sub-Cortical Lesion only] RH_Cort = 0 [No Cortical Right Hemisphere Lesion] RH_SubCort = 0 [No Sub-Cortical Right Hemisphere Lesion] Other_Location = 0 [No Lesion elsewhere] Hemisphere = Left [Left Hemisphere only]
This resulted in 54 images being selected from the ATLAS set. Thus we included a total of 99 images altogether across the three datasets. We divided these into two groups, ATLAS or Kessler+MCW, for cross-study comparisons. We then combined them to perform a five-fold cross-validation across all 99 images.
2.6 Comparison of CNN Methods
We compared our CNN to three state of the art recently published CNNs shown below. Our system was implemented using Pytorchpaszke2017automatic , the source code for which is available on our GitHub site available.upon.acceptanceruder2016overview
of 0.9 and weight decay of .0001. We use a batch size of 32, starting from an initial learning rate of 0.01 with a 3% weight decay after each epoch for a total of 50 epochs. In DeepMedic we use the default settings of learning rate of 0.001, the RMSProp optimizerruder2016overview with a weight decay of .0001, batch size of 10, and a total of 20 epochs.
DeepMedic kamnitsas2017efficient : This is a popular dual-path 3D convolutional neural network with a conditional random field to account for temporal order of slices. DeepMedic contains a path for low- and a separate path for high-resolution of images. Its success was demonstrated by winning the ISLES 2015 competition to identify brain injuries, tumors, and stroke lesions. The code for implementing DeepMedic is freely available on GitHub, https://github.com/Kamnitsask/deepmedic.
UResNet guerrero2018white : This is a convolutional neural network with residual connections he2016deep . The code for implementing UResNet is also freely available on GitHub, https://github.com/DeepLearnPhysics/pytorch-uresnet.
UNet ronneberger2015u : The was the original convolutional U-network proposed for biomedical image processing. Its code is also available on GitHub,
Two other non-CNN-based machine learning packages were also included because they have been made freely available to the brain imaging community and have been developed for ease of use. Both take a patch-based approach to automating lesion segmentation. That is, these methods convert the input image into multiple patches that are used to train the model. They are LINDA pustina2016automated , based on a random forests algorithm, and a second method based on Gaussian naive Bayes griffis2016voxel .
2.7 Data analysis
2.7.1 Measure of accuracy: Dice coefficient
The Dice coefficient is typically used to measure the accuracy of predicted lesions in MRI images zijdenbos1994morphometric . The output of our system and that of other methods is a binary mask of the dimensions as the input image, but with a 1 for each voxel calculated to contain a lesion, and a 0 otherwise. Comparison of the human expert-segmented lesion mask with that from automated methods is quantified with the Dice coefficient. Starting with the human binary mask as ground truth, each predicted voxel is determined to be either a true positive (TP, also one in true mask), false positive (FP, predicted as one but zero in the true mask), or false negative (FN, predicted as zero but one in the true mask). The Dice coefficient is formally defined as
2.7.2 Measure of statistical significance: Wilcoxon rank sum test
The Wilcoxon rank sum test wilcoxon1945individual
(also known as the Mann-Whitney U test) can be used to determine whether the difference between two sets of measurements is significant. More formally, it tests for the null hypothesis that a randomly selected point from a sample is equally likely to be lower or higher than a randomly selected one from a second sample. It is a non-parametric test for whether two sets of observations are likely to be from different distributions, without assuming a particular shape for those distributions.
In the results presented below, we take the rare and rigorous step of performing cross-study validations across independent datasets bernau2014cross . We also examine results from cross-validation in the combined dataset from the three different sources (KES, MCW, and ATLAS).
3.1 Cross-study validation results
To create relatively balanced sets in terms of number of scans, we combine the KES and MCW datasets into one. This yielded 45 samples in KES+MCW and 54 in ATLAS. We first train all convolutional neural networks (CNNs) on the KES+MCW data and test their ability to predict lesion locations in the ATLAS set. We then repeat the same procedure but with the train and test datasets reversed. Since LINDA and GNB come pre-trained and were intended for out-of-the-box use rather than re-training, we ran them as-is. Both programs have skull-removal built into their pipelines. Because the ATLAS images were the largest dataset with the skull still intact, we restricted our test of the LINDA and GNB methods to the ATLAS dataset.
3.1.1 Train on KES+MCW, predict on ATLAS
Figure 3 shows the Dice coefficient values on the ATLAS test dataset with training performed on KES and MCW images. Results show that the current system, with a median Dice value of 0.66, yielded the best performance. This was not just due to a few high values, as its Dice values generally clustered toward the higher end. The Dice values of UNet, UResNet, and DeepMedic Dice have a more even distribution than our system and lower median values. Both LINDA and GNB have Dice values clustered toward the lower end. Figure 3 also shows that our system has the highest mean Dice value. This value is reliably higher than all other methods under the Wilcoxon rank test wilcoxon1945individual (p 0.001). All the convolutional networks achieve better median values than LINDA and GNB.
Raincloud plots of Dice coefficient values of all models trained on KES+MCW and tested on ATLAS. For each method we show the distribution of Dice coefficients across all test images as well as the five summary values: median (middle horizontal line), third quartile (upper horizontal line), first quartile (lower horizontal line), min (lowermost bar), and max (uppermost bar). All models except for LINDA and GNB are trained on KES+MCW. The Table below the graph contains the mean Dice coefficients of all models on the ATLAS test data.
3.1.2 Train on ATLAS, predict on KES+MCW
Figure 4 shows results from the other direction of the cross-study analysis: training on ATLAS and testing on KES+MCW. In this case, although our system has the highest median, its distribution of Dice values is no longer clustered toward the high end as it was previously. The mean Dice value of our system is marginally above that of UNet alone and not statistically distinguishable from it. Compared to UResNet and DeepMedic, however, our method performs better, as shown from its reliably higher Dice values (p 0.001).
3.2 Cross-validation results on all datasets ATLAS, KES, and MCW combined
To take full advantage or our relatively large dataset, we combined images from all three sources to produce an overall dataset of 99 samples. We then performed a five-fold cross-validation on this combined dataset to evaluate the accuracy of each method. Figure 5 shows that our system again has the highest median Dice value. Our system also has the highest mean Dice value at 0.62, performing reliably better than the next best system, UNet, at 0.58. Indeed, our system performed better (p 0.001) than all three of the other CNN-based systems.
In addition to reporting this advantageous numeric performance of our system, an overall illustration of how the lesion masks produced by the current model compared to those from the other CNN-based models is in Figure 6. The expert-traced lesions (A) are shown alongside those produced by our system (B) and the models (C-E).
One point to note is that while our system performed significantly better in terms of overlap with human expert tracings as measured by the Dice coefficient, visually all the automatic methods appear grossly similar to the human expert segmentations.
3.3 Distribution of Dice coefficients across lesion size
Lesions with dimensions less than mm were classified as small, and any lesions with dimensions greater than those were considered large. In Figure 7 we show a raincloud plot of Dice values obtained by our system in the cross-validation and cross-study settings.
Smaller lesions are generally harder to identify than larger ones griffis2016voxel ; pustina2016automated ; ito2018comparison . To compare performance between lesion sizes, we split the lesions into small and large categories based on the distribution of lesion sizes in the overall set.
In all three cases, our method does very well on large lesions. In fact, when we train on KES+MCW and predict on ATLAS, the median Dice is above 0.8 for large lesions. In the cross-validation on all data combined, our model is significantly better than all methods except for DeepMedic, with p-values below 0.05. An example of a larger lesion is shown in Figure 8. The output lesion masks in red show our method and the other three to be qualitatively similar. An apparent exception is DeepMedic, which misidentifies tissue in the right-hemisphere as being lesioned. This mis-identification would seem to be an exception, however, given the similar numeric performance between our method and DeepMedic.
Smaller lesions, on the other hand, are associated with lower median Dice values overall, as generally expected. DeepMedic has particular difficulty with smaller lesions, whereas our system shows significantly greater accuracy than DeepMedic and UResNet. Interestingly, the distribution of Dice values for small lesions clusters towards the high end in the cross-validation setup with the most training data (all three datasets combined). This suggests that still more data would enable the model to achieve better accuracy at identifying small lesions. An example of a smaller lesion classification for the combined data cross-validation scenario is shown in Figure 9. This figure shows how the similarity of the overall contours of the model-based lesion masks (C-F) match up with the hand-segmented lesion mask (B). It also illustrates the face validity of the Dice coefficient, where higher Dice values also qualitatively correspond better to the hand-segmented lesion mask.
CNNs are a type of neural network, and what neural networks learn depends on what information is in the training data plaut1996understanding . In the cross-study scenario where we train on KES+MCW and test on ATLAS, the distribution of Dice values for smaller lesions is spread somewhat uniformly. However, when the network is trained on the ATLAS data and tested on the KES+MCW set, performance is worse. Thus the general rule that the information in the training dataset largely determines what the model can learn is also shown here for detecting small lesions.
3.4 Consolidating multi-path outputs
Previous multi-path approaches use a majority vote to combine outputs from different paths lyksborg2015ensemble . We compare our 3D CNN for combining multi-path outputs to using the majority vote and a simple union. In the union method, if at least one pixel has a one across the paths then the aggregated output also has a one in that pixel. Figure 10 shows that the union clearly performs more poorly than majority vote and our 3D CNN. Between the two better performing methods, the 3D CNN is reliably better than majority vote by a 4% margin with a p-value of 0.004. Also compared to post-processing with majority vote, the Dice values of the 3D CNN are concentrated more towards the high end.
|3D CNN||Union||Majority vote|
3.5 Mulit-modal T1 vs. T1+FLAIR
Our basic U-Net model is multimodal (specifically, bimodal) in that it allows for different image formats. Since the current project is focused exclusively on left hemisphere lesions, we present the model with T1 and FLAIR image formats of the lesioned left hemisphere. Below in Table 1 we show the cross-validation accuracy of our model on the KES and MCW images. When presented together, there is no significant difference between the two. However, if we look at just KES images that contain smaller lesions (and more recent, in the less than 5-week post-stroke range), then adding FLAIR confers a significant advantage. In the case of MCW images only that have lesions exclusively in the chronic epoch (at least 6 months post-stroke), the T1 images alone actually result in better performance than when the corresponding FLAIR images are added. This pattern corresponds with the standard clinical observation that FLAIR scans are useful for more recent stroke lesions but less so for those in the chronic phase ricci1999comparison . Such correspondence lends additional face validity to our model.
|Data||T1||T1+FLAIR||Wilcoxon rank test p-value||Average lesion size (in pixels)|
Here we have created, trained, and tested a new multi-path 2.5D convolutional neural network. The fractional designation on the dimension comes from its use of nine different 2D paths, followed by concatenation of the learned features across the paths, which are then passed to a 3D CNN for post-processing. This 2.5D design combines flexible and efficient 2D paths that process the data in different canonical orientations and normalizations with a 3D CNN that combines the 2D features in a way that informs the final 3D image output. Comparison of our system to previous efforts shows that CNN-based systems outperform more traditional machine-learning approaches based on random forests or Gaussian naive Bayes algorithms. Compared to other CNN systems, our system shows reliably superior performance in its ability to automatically segment stroke lesions from healthy tissue in the left hemisphere.
As methods such as this continue to improve the automated segmentation of brain lesions, a question arises. How good is good enough? An intuitive answer to this question comes from human expert raters. As mentioned in the Introduction, human expert raters have been shown to produce lesion segmentations with overlapping volumes between raters in the 67% to 78% range neumann2009interrater ; fiez2000 ; liew2018large , though 73% may be a more realistic upper value given the highly expert raters and limited scope of the data used by Neumann et al. neumann2009interrater to obtain the 78% value. The Dice coefficient used here is a formal measure of degree of spatial overlap that ranges between 0 and 1. Therefore a Dice coefficient in the 0.67 range can be considered to be at the edge of the human expert gold standard. When combining the datasets and performing iterative training and testing using standard 5-fold cross-validation, the lesion traces from our model overlap with human experts with a mean Dice coefficient of 0.62. While the 0.67 to 0.73 human benchmark range should be interpreted with caution because those numbers are based on data that are not identical to the data considered here, the accuracy of our system relative to previous efforts does suggest that deep learning-based CNN methods are beginning to approach human expert level accuracy for stroke lesion segmentation.
4.1 Future directions
An alternative to our system is to have a multi-modal 3D U-Net instead of the current 2D ones. While promising, it may be difficult to implement in practice. Training a 3D CNN involves adjusting many more parameters than for a 2D CNN, and would therefore require more data to train. A second future direction is to extend our current left hemisphere-focused system to include lesions to the right hemisphere. This extension should be relatively straightforward, as nothing is preventing our current system from being trained and tested on images with lesions to either hemisphere.
We have presented a multi-path, multi-modal convolutional neural network system for identifying lesions in brain MRI images. While the data with which our model is trained and tested includes exclusively left hemisphere lesions, our model can be trained and tested on lesions present anywhere in the brain. In cross-study and cross-validation tests, our model shows superior performance compared to existing CNN and non-CNN based machine learning methods for lesion identification. Our method extends previous efforts showing relatively high segmentation accuracy for large lesions. Given sufficient data, it markedly improves on previous efforts by being able to segment smaller lesions as well. We provide freely available open source code to train and test our model.
This advance in performance is critically significant, as it brings the field closer to removing the bottleneck of having human experts spend numerous hours hand-segmenting brain lesions on MRI scans. Once automated methods are sufficiently accurate and widely available, they will free up researchers to focus their time on other critical aspects of neuropsychological data acquisition and analysis. The hope is this re-allocation of expert resources will help advance the pace at which we can further our understanding of the critical neural basis of thinking and behavior.
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