Brain Segmentation
60 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find Brain Segmentation models and implementationsLatest papers with no code
Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation
Our system has two components: 1) a segmentation network on the server side which processes the image mixture, and 2) a segmentation unmixing network which recovers the correct segmentation map from the segmentation mixture.
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering
Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data.
Semi-supervised Learning with Robust Loss in Brain Segmentation
In this work, we used a semi-supervised learning method to train deep learning model that can segment the brain MRI images.
Contour Dice loss for structures with Fuzzy and Complex Boundaries in Fetal MRI
In this paper, we study the use of the Contour Dice loss for both problems and compare it to other boundary losses and to the combined Dice and Cross-Entropy loss.
TBI-GAN: An Adversarial Learning Approach for Data Synthesis on Traumatic Brain Segmentation
To address these issues, we propose a novel medical image inpainting model named TBI-GAN to synthesize TBI MR scans with paired brain label maps.
Comparative Validation of AI and non-AI Methods in MRI Volumetry to Diagnose Parkinsonian Syndromes
Dice scores of both DL models were sufficiently high (>0. 85), and their AUCs for disease classification were superior to that of FS.
Suggestive Annotation of Brain MR Images with Gradient-guided Sampling
We evaluate the framework on two different brain image analysis tasks, namely brain tumour segmentation and whole brain segmentation.
CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development.
Ischemic Stroke Lesion Segmentation Using Adversarial Learning
Training a segmentation network along with an adversarial network can detect and correct higher order inconsistencies between the segmentation maps produced by ground-truth and the Segmentor.
Learning to segment fetal brain tissue from noisy annotations
However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures.