Brain Segmentation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

5 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 65 papers with code (146 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
  7 Mar 2018 9 repositories listed
  Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications.
• QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
  12 Jan 2018 6 repositories listed
  We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a \revision{MRI brain scan} in 20 seconds.
• Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
  9 Jun 2019 4 repositories listed
  Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.
• MRI Super-Resolution using Multi-Channel Total Variation
  8 Oct 2018 4 repositories listed
  This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast.
• CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
  8 Jan 2022 3 repositories listed
  The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137).
• A Learning Strategy for Contrast-agnostic MRI Segmentation
  4 Mar 2020 3 repositories listed Syntology ran 0 of 9 samples · 9 unverified
  These samples are produced using the generative model of the classical Bayesian segmentation framework, with randomly sampled parameters for appearance, deformation, noise, and bias field.
• HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation
  9 Apr 2018 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation.
• 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
  12 Dec 2016 3 repositories listed
  To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data.
• VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation
  21 Aug 2016 3 repositories listed
  Recently deep residual learning with residual units for training very deep neural networks advanced the state-of-the-art performance on 2D image recognition tasks, e.
• GOUHFI: a novel contrast- and resolution-agnostic segmentation tool for Ultra-High Field MRI
  16 May 2025 2 repositories listed
  Hence, we propose a novel Deep Learning (DL)-based segmentation technique called GOUHFI: Generalized and Optimized segmentation tool for Ultra-High Field Images, designed to segment UHF images of various contrasts and…
• Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging
  28 Nov 2023 2 repositories listed Syntology ran 12 of 15 samples · 3 unverified
  We present new metrics to validate the intra- and inter-subject robustness of Brain-ID features, and evaluate their performance on four downstream applications, covering contrast-independent (anatomy…
• FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain
  30 Aug 2022 2 repositories listed
  A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions.
• Partial supervision for the FeTA challenge 2021
  3 Nov 2021 2 repositories listed
  Label-set loss functions allow to train deep neural networks with partially segmented images, i.
• Importance Driven Continual Learning for Segmentation Across Domains
  30 Apr 2020 2 repositories listed
  The ability of neural networks to continuously learn and adapt to new tasks while retaining prior knowledge is crucial for many applications.
• `Project & Excite' Modules for Segmentation of Volumetric Medical Scans
  11 Jun 2019 2 repositories listed
  Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging.
• 3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
  28 Mar 2019 2 repositories listed
  To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location.
• Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control
  24 Nov 2018 2 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  Next to voxel-wise uncertainty, we introduce four metrics to quantify structure-wise uncertainty in segmentation for quality control.
• Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
  1 Jun 2018 2 repositories listed
  Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy.
• Deep Neural Networks for Anatomical Brain Segmentation
  9 Feb 2015 2 repositories listed
  To our knowledge, our technique is the first to tackle the anatomical segmentation of the whole brain using deep neural networks.
• Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release
  30 Jan 2025 1 repository listed
  We are releasing a state-of-the-art model for whole-head MRI segmentation, along with a dataset of 61 clinical MRIs and training labels, including non-brain structures.
• Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning
  21 Jan 2025 1 repository listed
  Self-supervised deep learning has accelerated 2D natural image analysis but remains difficult to translate into 3D MRI, where data are scarce and pre-trained 2D backbones cannot capture volumetric context.
• Comparative Study of Probabilistic Atlas and Deep Learning Approaches for Automatic Brain Tissue Segmentation from MRI Using N4 Bias Field Correction and Anisotropic Diffusion Pre-processing Techniques
  8 Nov 2024 1 repository listed
  The findings highlight the superiority of nnU-Net models in brain tissue segmentation, particularly when combined with N4 Bias Field Correction and Anisotropic Diffusion pre-processing techniques.
• Bilateral Hippocampi Segmentation in Low Field MRIs Using Mutual Feature Learning via Dual-Views
  23 Oct 2024 1 repository listed
  Accurate hippocampus segmentation in brain MRI is critical for studying cognitive and memory functions and diagnosing neurodevelopmental disorders.
• MedSegMamba: 3D CNN-Mamba Hybrid Architecture for Brain Segmentation
  12 Sep 2024 1 repository listed
  The proposed model achieved the highest overall performance across all metrics (DSC 0.
• Segment anything model (SAM) for brain extraction in fMRI studies
  9 Jan 2024 1 repository listed
  Brain extraction and removal of skull artifacts from magnetic resonance images (MRI) is an important preprocessing step in neuroimaging analysis.
• UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation
  19 Sep 2023 1 repository listed
  The different predictions in these duplicated heads are used to obtain pseudo labels for unlabeled target-domain images and their uncertainty to identify reliable pseudo labels.
• Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentation
  11 Sep 2023 1 repository listed
  We then evaluate the synthetic learning approach and confirm its robustness to variations in image contrast by reporting the capacity of such a model to segment both T1- and T2-weighted images from the same individuals.
• Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration
  8 Sep 2023 1 repository listed
  Subsequently, the model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are available.
• Two Independent Teachers are Better Role Model
  9 Jun 2023 1 repository listed
  In this work, we address the above limitations by designing a new deep-learning model, called 3D-DenseUNet, which works as adaptable global aggregation blocks in down-sampling to solve the issue of spatial information…
• Model-based inexact graph matching on top of CNNs for semantic scene understanding
  18 Jan 2023 1 repository listed
  On FASSEG data, results show that our module improves accuracy of the CNN by about 6.

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.