MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research.The pipeline is based on nn-UNet and has the capability to segment 120 unique tissue classes from a whole-body 18F-FDG PET/CT image.

Preprocessing pipeline on Brain MR Images through FSL and ANTs, including registration, skull-stripping, bias field correction, enhancement and segmentation.

Fully automatic brain tumour segmentation using Deep 3-D convolutional neural networks

This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"

This repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios.

Deep learning based skull stripping and FLAIR abnormality segmentation in brain MRI using U-Net

The project is used to do preprocessing on brain MR images by using Nipype.

PNH segmentation pipelines based on nipype

3D Multi-modal (FLAIR and T1) Brain MRI Scan Segmentation using Generative Adversarial Learning

[ECCV 2024] Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging

AICONSlab's brain extraction (skull-stripping) algorithm using CNNs

AICONSlab's ventricular segmentation technique using CNNs

[MICCAI'23] "Learning Ontology-Based Hierarchical Structural Relationship for Whole Brain Segmentation".

3D Slicer extension that provides T2-FLAIR MRI manipulation methods in order to increase contrast and segment hyperintense Multiple Sclerosis lesions

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Brain Segmentation