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Found 18 papers, 7 papers with code
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.
Metrics reloaded: Recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
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.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
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.
Domain Adaptive Segmentation in Volume Electron Microscopy Imaging
In this work, we extend recently proposed classification DA techniques to an encoder-decoder layout and propose a novel method that adds a reconstruction decoder to the classical encoder-decoder segmentation in order to align source and target encoder features.
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.
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.
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.
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.
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.
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.
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.
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.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis
These include standards on data formats, metadata, data presentation and sharing, and incentives to generate new datasets.
Enabling Global Image Data Sharing in the Life Sciences
Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences.
