3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction

Abstract

Past few years have witnessed the prevalence of deep learning in many application scenarios, among which is medical image processing. Diagnosis and treatment of brain tumors requires an accurate and reliable segmentation of brain tumors as a prerequisite. However, such work conventionally requires brain surgeons significant amount of time. Computer vision techniques could provide surgeons a relief from the tedious marking procedure. In this paper, a 3D U-net based deep learning model has been trained with the help of brain-wise normalization and patching strategies for the brain tumor segmentation task in the BraTS 2019 competition. Dice coefficients for enhancing tumor, tumor core, and the whole tumor are 0.737, 0.807 and 0.894 respectively on the validation dataset. These three values on the test dataset are 0.778, 0.798 and 0.852. Furthermore, numerical features including ratio of tumor size to brain size and the area of tumor surface as well as age of subjects are extracted from predicted tumor labels and have been used for the overall survival days prediction task. The accuracy could be 0.448 on the validation dataset, and 0.551 on the final test dataset.

arXiv commentThird place award of the 2019 MICCAI BraTS challenge survival task [BraTS 2019](https://www.med.upenn.edu/cbica/brats2019.html)

References in corpus (2)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Instance Normalization: The Missing Ingredient for Fast Stylization

Cited by in corpus (5)

• TransBTS: Multimodal Brain Tumor Segmentation Using Transformer
• Medical Image Segmentation Using Squeeze-and-Expansion Transformers
• Category Guided Attention Network for Brain Tumor Segmentation in MRI
• Advancing Brain Tumor Segmentation via Attention-based 3D U-Net Architecture and Digital Image Processing
• Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging