1. BraTs (Brain Tumor Segmentation)

1-1) Overview

Fig 1: Brain Complete Tumor Segmention

Fig 2: Brain Core Tumor Segmention

Ground Truth

Prediction

1-2) About

This project is a segmentation model to diagnose brain tumor (Complete, Core) using BraTS 2016, 2017 dataset.

BraTS has always been focusing on the evaluation of state-of-the-art methods for the segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) scans. BraTS 2018 utilizes multi-institutional pre- operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Furthemore, to pinpoint the clinical relevance of this segmentation task, BraTS’18 also focuses on the prediction of patient overall survival, via integrative analyses of radiomic features and machine learning algorithms.

1-3) Models

U-Net

pytorch/models/unet.py

Fig 3: U-Net Diagram

PSPNet

pytorch/models/pspnet.py

Fig 4: PSPNet Diagram

DeepLab V3 +

pytorch/models/deeplab.py

Fig 5: DeepLab V3+ Diagram

2. Dataset

2-1) Overview

Multimodal MRI Dataset

File: A File has Multi-Modal MRI Data of one person

File Format: nii.gz

Image Shape: 240(Slide Width) × 240(Slide Height) × 155(Number of Slide) × 4(Multi-mode)

Image Mode: 4 (Multi-mode)

channel 0 channel 1 channel 2 channel 3

Label Shape: channel 0: background

channel 1: necrotic and non-enhancing tumor

channel 2: edema

channel 3: enhancing tumor

2-2) Data Augmentation

2-3) Code

pytorch/preprocess.py
pytorch/dataset.py

preprocess.py: Code for data pre-processing. Using this, original image(240×240×155×4) can be diveded into 155 image pieces(240×240) of the specific mode. Also, original label(240×240×155) can be divided into 155 label pieces.

dataset.py: Code for Prepareing dataset and dataloader for Pytorch modules