Skull Stripping

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

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

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

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

Most implemented papers archive 2025-07-28

20 shown of 20 papers with code (39 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.

• CompNet: Complementary Segmentation Network for Brain MRI Extraction
  27 Mar 2018 8 repositories listed
  Brain extraction is a fundamental step for most brain imaging studies.
• A survey of loss functions for semantic segmentation
  26 Jun 2020 2 repositories listed
  In this paper, we have summarized some of the well-known loss functions widely used for Image Segmentation and listed out the cases where their usage can help in fast and better convergence of a model.
• BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis
  11 Jul 2025 1 repository listed
  BrainLesion Suite is a versatile toolkit for building modular brain lesion image analysis pipelines in Python.
• How We Won the ISLES'24 Challenge by Preprocessing
  23 May 2025 1 repository listed
  Stroke is among the top three causes of death worldwide, and accurate identification of stroke lesion boundaries is critical for diagnosis and treatment.
• Skull stripping with purely synthetic data
  12 May 2025 1 repository listed
  While many skull stripping algorithms have been developed for multi-modal and multi-species cases, there is still a lack of a fundamentally generalizable approach.
• DISARM++: Beyond scanner-free harmonization
  6 May 2025 1 repository listed
  Harmonization of T1-weighted MR images across different scanners is crucial for ensuring consistency in neuroimaging studies.
• PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation
  10 Apr 2025 1 repository listed
  In this work, we introduce PhaseGen, a novel complex-valued diffusion model for generating synthetic MRI raw data conditioned on magnitude images, commonly used in clinical practice.
• Pfungst and Clever Hans: Identifying the unintended cues in a widely used Alzheimer's disease MRI dataset using explainable deep learning
  27 Jan 2025 1 repository listed
  This study aims to enlighten the underlying black-box nature and reveal individual contributions of T1-weighted (T1w) gray-white matter texture, volumetric information and preprocessing on classification performance.
• Domain Influence in MRI Medical Image Segmentation: spatial versus k-space inputs
  1 Jul 2024 1 repository listed
  The attention-based models used are PerceiverIO and a vanilla Transformer encoder.
• Neural Pre-Processing: A Learning Framework for End-to-end Brain MRI Pre-processing
  21 Mar 2023 1 repository listed
  Head MRI pre-processing involves converting raw images to an intensity-normalized, skull-stripped brain in a standard coordinate space.
• Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?
  14 Dec 2022 1 repository listed
  Our experiments show that the choice of a BE method can compromise up to 15.
• k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
  19 May 2022 1 repository listed
  Results: Both datasets were very similar to the ground truth (DICE scores of 92\%-98\% and Hausdorff distances of under 5.
• Negligible effect of brain MRI data preprocessing for tumor segmentation
  11 Apr 2022 1 repository listed
  Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability.
• Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks
  4 Aug 2021 1 repository listed
  We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with lesions.
• Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast
  24 Dec 2020 1 repository listed
  Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well -…
• ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation
  13 Feb 2020 1 repository listed
  However, existing 2D deep learning methods are not equipped to effectively capture 3D spatial contextual information that is needed to achieve accurate brain structure segmentation.
• Training of a Skull-Stripping Neural Network with efficient data augmentation
  25 Oct 2018 1 repository listed
  Skull-stripping methods aim to remove the non-brain tissue from acquisition of brain scans in magnetic resonance (MR) imaging.
• Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Consensus-based Silver standard Masks
  13 Apr 2018 1 repository listed
  Our use of silver standard masks reduced the cost of manual annotation, decreased inter-intra-rater variability, and avoided CNN segmentation super-specialization towards one specific manual annotation guideline that…
• Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network
  2 Jul 2016 1 repository listed
  The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans.
• Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network
  2 Jul 2016 1 repository listed
  The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans.