Brain tumors segmentation on 3D MRI images. The model has been trained on BratTS20 and BraTS21 datasets, and now working with BraTS23.

Brainchop: In-browser 3D MRI rendering and segmentation

3D U-Net model for volumetric semantic segmentation written in pytorch

Utils and convenience functions for large-scale bio-image analysis.

Generate consensus 3D cells segmentations by combining 2D cell segmentations from any combination of xy, xz, yz views, compatible with outputs of any 2D segmentation method.

Distributed segmentation for bio-image-analysis

A suite of scripts and easy-to-follow tutorial to process point cloud data with Python

[ICCV2023] Official Implementation of "UniTR: A Unified and Efficient Multi-Modal Transformer for Bird’s-Eye-View Representation"

Abdomen 3D Segmentation Using UNETR: Tool for segmenting abdominal organs using the UNETR model. Combines Transformers with CNNs for precise 3D segmentation. Dive into medical imaging! 🏥✨🔍

Set of models for segmentation of 3D volumes

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.

The official implementation of SAGA (Segment Any 3D GAussians)

The MAMA-MIA Dataset: A Multi-Center Breast Cancer DCE-MRI Public Dataset with Expert Segmentations

Backend segmentation/classification library (using ONNX runtime) for Raidionics

Segment Anything in 3D with NeRFs (NeurIPS 2023)

[ICLR 2024] AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation

This is the official repo for Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation (ICCV 23).

This project focuses on the segmentation of brain tumors in 3D MRI images using Convolutional Neural Network (CNN) models. The research compares the performance of SegNet, V-Net, and U-Net architectures for brain tumor segmentation and evaluates them based on complexity, training time, and segmentation accuracy.