Volumetric Medical Image Segmentation

17 papers with code • 1 benchmarks • 2 datasets

This task has no description! Would you like to contribute one?

Libraries

Use these libraries to find Volumetric Medical Image Segmentation models and implementations

Latest papers with no code

D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation

no code yet • 15 Mar 2024

D-Net is able to effectively utilize a multi-scale large receptive field and adaptively harness global contextual information.

Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors

no code yet • 12 Oct 2023

In this paper, we propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation and aims to its improve boundary prediction.

Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder

no code yet • 15 Jun 2023

Masked autoencoder (MAE) is a promising self-supervised pre-training technique that can improve the representation learning of a neural network without human intervention.

Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation

no code yet • 23 Mar 2023

Recently, the advent of vision Transformer (ViT) has brought substantial advancements in 3D dataset benchmarks, particularly in 3D volumetric medical image segmentation (Vol-MedSeg).

MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling

no code yet • 16 Mar 2023

In this study, we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg), a $\textbf{unified}$ UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation.

Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift

no code yet • 4 Oct 2022

At the saddle point of the underlying objective, the weights assign label-dense samples to the supervised loss and label-sparse samples to the unsupervised consistency regularization.

Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma

no code yet • 9 Aug 2022

It can be separated into two regions, intrameatal and extrameatal respectively corresponding to being inside or outside the inner ear canal.

Distributed Contrastive Learning for Medical Image Segmentation

no code yet • 7 Aug 2022

However, when adopting CL in FL, the limited data diversity on each site makes federated contrastive learning (FCL) ineffective.

Implicit U-Net for volumetric medical image segmentation

no code yet • 30 Jun 2022

U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images.

Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation

no code yet • 14 Jun 2022

For 3D medical image (e. g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly.