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Found 8 papers, 7 papers with code
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Deep Anatomical Federated Network (Dafne): an open client/server framework for the continuous collaborative improvement of deep-learning-based medical image segmentation
Semantic segmentation is a crucial step to extract quantitative information from medical (and, specifically, radiological) images to aid the diagnostic process, clinical follow-up.
TotalSegmentator: robust segmentation of 104 anatomical structures in CT images
The model significantly outperformed another publicly available segmentation model on a separate dataset (Dice score, 0. 932 versus 0. 871, respectively).
Combined tract segmentation and orientation mapping for bundle-specific tractography
It is based on a learned mapping from the original fiber orientation distribution function (FOD) peaks to tract specific peaks, called tract orientation maps.
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
The U-Net was presented in 2015.
Ranked #2 on
Medical Image Segmentation
on Synapse multi-organ CT
Tract orientation mapping for bundle-specific tractography
While the major white matter tracts are of great interest to numerous studies in neuroscience and medicine, their manual dissection in larger cohorts from diffusion MRI tractograms is time-consuming, requires expert knowledge and is hard to reproduce.
TractSeg - Fast and accurate white matter tract segmentation
The individual course of white matter fiber tracts is an important key for analysis of white matter characteristics in healthy and diseased brains.
Direct White Matter Bundle Segmentation using Stacked U-Nets
We present a novel supervised approach for direct tract segmentation that shows major performance gains.
