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medical-4

3D Medical Imaging Segmentation

3260 papers • 126 benchmarks • 313 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 )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in 3d-medical-imaging-segmentation-9

Trend

Dataset

Best Model

Actions

TCIA Pancreas-CT

TCIA Pancreas-CT

Libraries

i

Use these libraries to find 3d-medical-imaging-segmentation-9 models and implementations

black0017/MedicalZooPytorch

3 papers 1,700

Datasets

Verse

SKM-TEA

ATLAS v2.0

KiTS19

RAD-ChestCT Dataset

RAD-ChestCT Dataset

HaN-Seg

Subtasks

Pancreas Segmentation

Most implemented papers

UNETR: Transformers for 3D Medical Image Segmentation

H. Roth, Daguang Xu, Dong Yang, Ali Hatamizadeh•Wed Mar 17 2021

This work reformulates the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem and introduces a novel architecture, dubbed as UNEt TRansformers (UNETR), that utilizes a transformer as the encoder to learn sequence representations of the input volume and effectively capture the global multi-scale information.

2322

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

EPISURG

EPISURG

MRSpineSeg Challenge

MRSpineSeg Challenge

BHSD

0

BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

F. M. Calisto, N. Nunes, J. Nascimento•Mon Apr 06 2020

The field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening is described and recommendations from the radiologists for guiding the future design of medical imaging interfaces are summarized.

44 0

Med3D: Transfer Learning for 3D Medical Image Analysis

Sihong Chen, Kai Ma, Yefeng Zheng•Sun Mar 31 2019

A heterogeneous 3D network called Med3D is designed to co-train multi-domain 3DSeg-8 so as to make a series of pre-trained models which can accelerate the training convergence speed of target 3D medical tasks and improve accuracy ranging from 3% to 20%.

593 0

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

M. Mazurowski, Mateusz Buda, Ashirbani Saha•Fri May 31 2019

A fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and test whether these characteristics are predictive of tumor genomic subtypes is proposed.

316 0

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study

Christian Desrosiers, Ismail Ben Ayed, J. Dolz•Sun Dec 11 2016

This work is the first to study subcortical structure segmentation on such large‐scale and heterogeneous data and yielded segmentations that are highly consistent with a standard atlas‐based approach, while running in a fraction of the time needed by atlas-based methods and avoiding registration/normalization steps.

378 0

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

Enforcing temporal consistency in Deep Learning segmentation of brain MR images

Malav Bateriwala, P. Bourgeat•Wed Jun 12 2019

Proposed CNN based segmentation approaches demonstrate how 2D segmentation using prior slices can provide similar results to 3D segmentations while maintaining good continuity in the 3D dimension and improved speed.

4 0

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

Adding a benchmark result helps the community track progress.

3D Medical Imaging Segmentation | State-of-the-Art