Efficient Connectivity-Preserving Instance Segmentation with Supervoxel-Based Loss Function
Reconstructing the intricate local morphology of neurons and their long-range projecting axons can address many connectivity related questions in neuroscience. The main bottleneck in connectomics pipelines is correcting topological errors, as multiple entangled neuronal arbors is a challenging instance segmentation problem. More broadly, segmentation of curvilinear, filamentous structures continues to pose significant challenges. To address this problem, we extend the notion of simple points from digital topology to connected sets of voxels (i.e. supervoxels) and propose a topology-aware neural network segmentation method with minimal computational overhead. We demonstrate its effectiveness on a new public dataset of 3-d light microscopy images of mouse brains, along with the benchmark datasets DRIVE, ISBI12, and CrackTree.
Code (1)
Tasks
Instance SegmentationSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
Preserving instance continuity and length in segmentation through connectivity-aware loss computation
In many biomedical segmentation tasks, the preservation of elongated structure continuity and length is more important than voxel-wise accuracy. We propose two novel loss functions, Negative Centerline Loss and Simplifie…
Actor-Action Semantic Segmentation with Grouping Process Models
Actor-action semantic segmentation made an important step toward advanced video understanding problems: what action is happening; who is performing the action; and where is the action in space-time. Current models for th…
Semantic SegmentationVideo UnderstandingStructure-Preserving Instance Segmentation via Skeleton-Aware Distance Transform
Objects with complex structures pose significant challenges to existing instance segmentation methods that rely on boundary or affinity maps, which are vulnerable to small errors around contacting pixels that cause notic…
Image SegmentationInstance SegmentationObjectSegmentation+1Toward better boundary preserved supervoxel segmentation for 3D point clouds
Supervoxels provide a more natural and compact representation of three dimensional point clouds, and enable the operations to be performed on regions rather than on the scattered points. Many state-of-the-art supervoxel …
Point Cloud SegmentationSegmentationSaliency-guided Adaptive Seeding for Supervoxel Segmentation
We propose a new saliency-guided method for generating supervoxels in 3D space. Rather than using an evenly distributed spatial seeding procedure, our method uses visual saliency to guide the process of supervoxel genera…
Segmentation