H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation

06/02/2022
by   Peixian Liang, et al.
12

Deep learning (DL) based semantic segmentation methods have achieved excellent performance in biomedical image segmentation, producing high quality probability maps to allow extraction of rich instance information to facilitate good instance segmentation. While numerous efforts were put into developing new DL semantic segmentation models, less attention was paid to a key issue of how to effectively explore their probability maps to attain the best possible instance segmentation. We observe that probability maps by DL semantic segmentation models can be used to generate many possible instance candidates, and accurate instance segmentation can be achieved by selecting from them a set of "optimized" candidates as output instances. Further, the generated instance candidates form a well-behaved hierarchical structure (a forest), which allows selecting instances in an optimized manner. Hence, we propose a novel framework, called hierarchical earth mover's distance (H-EMD), for instance segmentation in biomedical 2D+time videos and 3D images, which judiciously incorporates consistent instance selection with semantic-segmentation-generated probability maps. H-EMD contains two main stages. (1) Instance candidate generation: capturing instance-structured information in probability maps by generating many instance candidates in a forest structure. (2) Instance candidate selection: selecting instances from the candidate set for final instance segmentation. We formulate a key instance selection problem on the instance candidate forest as an optimization problem based on the earth mover's distance (EMD), and solve it by integer linear programming. Extensive experiments on eight biomedical video or 3D datasets demonstrate that H-EMD consistently boosts DL semantic segmentation models and is highly competitive with state-of-the-art methods.

READ FULL TEXT

page 1

page 3

page 5

page 6

page 9

page 10

research
12/06/2018

PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding

We present PartNet: a consistent, large-scale dataset of 3D objects anno...
research
07/04/2017

The Candidate Multi-Cut for Cell Segmentation

Two successful approaches for the segmentation of biomedical images are ...
research
02/15/2020

Cell R-CNN V3: A Novel Panoptic Paradigm for Instance Segmentation in Biomedical Images

Instance segmentation is an important task for biomedical image analysis...
research
03/01/2020

3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentation via Coupled Feature Selection

We propose a novel fast and robust 3D point clouds segmentation framewor...
research
04/07/2017

Learning Where to Look: Data-Driven Viewpoint Set Selection for 3D Scenes

The use of rendered images, whether from completely synthetic datasets o...

Please sign up or login with your details

Forgot password? Click here to reset