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medical-8

Volumetric Medical Image Segmentation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in volumetric-medical-image-segmentation-16

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Dataset

Best Model

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PROMISE 2012

Libraries

Use these libraries to find volumetric-medical-image-segmentation-16 models and implementations

black0017/MedicalZooPytorch

3 papers 1,602

Datasets

PROMISE12

Radio-Freqency Ultrasound volume dataset for pre-clinical liver tumors

Subtasks

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Most implemented papers

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

F. Milletarì, N. Navab, Seyed-Ahmad Ahmadi•Tue Jun 14 2016

This work proposes an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network, trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once.

9799

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Paper Graph

On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

Wenqi Li, S. Ourselin, Tom Kamiel Magda Vercauteren, M. Jorge Cardoso, Guotai Wang, Lucas Fidon•Sat Jun 24 2017

This work investigates efficient and flexible elements of modern convolutional networks such as dilated convolution and residual connection, and proposes a high-resolution, compact Convolutional network for volumetric image segmentation.

354 0

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nnFormer: Interleaved Transformer for Volumetric Segmentation

Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, Yizhou Yu•Thu Dec 31 2020

nnFormer is introduced, a powerful segmentation model with an interleaved architecture based on empirical combination of self-attention and convolution that achieves tremendous improvements over previous transformer-based methods on two commonly used datasets Synapse and ACDC.

429 0

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Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation

Miguel Monteiro, Mário A. T. Figueiredo, Arlindo L. Oliveira•Wed Jul 18 2018

This paper developed an implementation of the Conditional Random Field as a Recurrent Neural Network layer which works for any number of spatial dimensions, input/output image channels, and reference image channels and concluded that the performance differences observed were not statistically significant.

28 0

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Positional Contrastive Learning for Volumetric Medical Image Segmentation

Dewen Zeng, Meiping Huang, Zhuang Jian, Yiyu Shi, Xiaowei Xu, Yawen Wu, Xinrong Hu, Haiyun Yuan, Jingtong Hu•Tue Jun 15 2021

A novel positional contrastive learning (PCL) framework is proposed to generate contrastive data pairs by leveraging the position information in volumetric medical images to substantially improve the segmentation performance compared to existing methods in both semi-supervised setting and transfer learning setting.

105 0

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KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation

Vishal M. Patel, Vishwanath A. Sindagi, I. Hacihaliloglu, Jeya Maria Jose Valanarasu•Sat Oct 03 2020

A new architecture for im- age segmentation- KiU-Net is designed which has two branches: an overcomplete convolutional network Kite-Net which learns to capture fine details and accurate edges of the input, and (2) U- net which learns high level features.

200 0

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A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

Munawar Hayat, Himashi Peiris, Zhaolin Chen, Gary F. Egan, Mehrtash Harandi•Thu Nov 25 2021

A Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume, is proposed.

195 0

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Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images

Wenjia Bai, Yuan Wang, Laura Blackie, I. Miguel-Aliaga•Mon May 30 2022

Experiments show that the proposed novel memory-efficient network architecture for 3D high-resolution image segmentation outperforms state-of-the-art 3D segmentation methods in terms of both segmentation accuracy and memory efficiency.

5 0

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Volumetric Medical Image Segmentation | State-of-the-Art