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Found 17 papers, 7 papers with code
Joint Semantic Instance Segmentation on Graphs with the Semantic Mutex Watershed
Semantic instance segmentation is the task of simultaneously partitioning an image into distinct segments while associating each pixel with a class label.
ViM-UNet: Vision Mamba for Biomedical Segmentation
Here, we introduce ViM-UNet, a novel segmentation architecture based on it and compare it to UNet and UNETR for two challenging microscopy instance segmentation tasks.
Segment Anything for Microscopy
We present Segment Anything for Microscopy, a tool for interactive and automatic segmentation and tracking of objects in multi-dimensional microscopy data.
Probabilistic Domain Adaptation for Biomedical Image Segmentation
We further study joint and separate source-target training strategies and evaluate our method on three challenging domain adaptation tasks for biomedical segmentation.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Stateless actor-critic for instance segmentation with high-level priors
We formulate the instance segmentation problem as graph partitioning and the actor critic predicts the edge weights driven by the rewards, which are based on the conformity of segmented instances to high-level priors on object shape, position or size.
Reinforcement learning for instance segmentation with high-level priors
We formulate the instance segmentation problem as graph partitioning and the actor critic predicts the edge weights driven by the rewards, which are based on the conformity of segmented instances to high-level priors on object shape, position or size.
Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddings
Most state-of-the-art instance segmentation methods have to be trained on densely annotated images.
Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks
This work introduces a new proposal-free instance segmentation method that builds on single-instance segmentation masks predicted across the entire image in a sliding window style.
Accurate and versatile 3D segmentation of plant tissues at cellular resolution
Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs.
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation
Semantic instance segmentation is the task of simultaneously partitioning an image into distinct segments while associating each pixel with a class label.
Synthetic patches, real images: screening for centrosome aberrations in EM images of human cancer cells
Recent advances in high-throughput electron microscopy imaging enable detailed study of centrosome aberrations in cancer cells.
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation
We propose a theoretical framework that generalizes simple and fast algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes.
Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted Multicuts
The throughput of electron microscopes has increased significantly in recent years, enabling detailed analysis of cell morphology and ultrastructure.
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning
Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold.
The Mutex Watershed: Efficient, Parameter-Free Image Partitioning
Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments; or equivalently, the task of detecting closed contours in an image.
Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain
Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems.
