3D Medical Imaging Segmentation
32 papers with code • 1 benchmarks • 9 datasets
3D medical imaging segmentation is the task of segmenting medical objects of interest from 3D medical imaging.
( Image credit: Elastic Boundary Projection for 3D Medical Image Segmentation )
Benchmarks
These leaderboards are used to track progress in 3D Medical Imaging Segmentation
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Libraries
Use these libraries to find 3D Medical Imaging Segmentation models and implementationsDatasets
VerSe
SKM-TEA
KiTS19
ATLAS v2.0
HaN-Seg
BHSD
Latest papers
FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images
Segment anything models (SAMs) are gaining attention for their zero-shot generalization capability in segmenting objects of unseen classes and in unseen domains when properly prompted.
nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model
lhaof/nnmamba
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Extensive experiments on 6 datasets demonstrate nnMamba's superiority over state-of-the-art methods in a suite of challenging tasks, including 3D image segmentation, classification, and landmark detection.
SegReg: Segmenting OARs by Registering MR Images and CT Annotations
This manual process is highly time-consuming and expensive, limiting the number of patients who can receive timely radiotherapy.
Reliable brain morphometry from contrast‐enhanced T1w‐MRI in patients with multiple sclerosis
SCAN-NRAD/DL-DiReCT
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Human Brain Mapping 2022
The segmentations were derived with FreeSurfer from the non-enhanced image and used as ground truth for the coregistered CE image.
An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation
In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans.
Dynamic Linear Transformer for 3D Biomedical Image Segmentation
Transformer-based neural networks have surpassed promising performance on many biomedical image segmentation tasks due to a better global information modeling from the self-attention mechanism.
An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation
The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks.
Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus
UCSB-VRL/NPH_Prediction
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BME Frontiers 2022
The segmentation and network features are used to train a model for NPH prediction.
UNETR: Transformers for 3D Medical Image Segmentation
Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem.
Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation
DL+DiReCT is a promising combination of a deep learning‐based method with a traditional registration technique to detect subtle changes in cortical thickness.
