Brain Image Segmentation
17 papers with code • 6 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Brain Image Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
CREMI
Latest papers
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
Brain extraction, registration and segmentation are indispensable preprocessing steps in neuroimaging studies.
Boosting multiple sclerosis lesion segmentation through attention mechanism
ictlab-unict/attention-cnn-MS-segmentation
•
•
Computers in Biology and Medicine 2023
Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and monitoring its progression.
ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data
erneternet/ernet
•
•
Our code and data can be found at https://github. com/ERNetERNet/ERNet
Learning from imperfect training data using a robust loss function: application to brain image segmentation
Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications.
Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images
dogabasaran/lesion-synthesis
•
•
In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images.
An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation
In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans.
DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation
dinhhieuhoang/dam-ca-infantbrain
•
•
While Magnetic Resonance Imaging (MRI) has played an essential role in infant brain analysis, segmenting MRI into a number of tissues such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is crucial and complex due to the extremely low intensity contrast between tissues at around 6-9 months of age as well as amplified noise, myelination, and incomplete volume.
A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis.
A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients.
Using deep convolutional neural networks for neonatal brain image segmentation
Introduction: Deep learning neural networks are especially potent at dealing with structured data, such as images and volumes.
