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

TL;DR

This paper presents a 3D U-Net deep learning model for brain tumor segmentation and survival prediction, achieving high accuracy in tumor delineation and moderate success in survival days estimation, aiding clinical diagnosis.

Contribution

Introduces a 3D U-Net based approach with normalization and patching strategies for improved brain tumor segmentation and combines tumor features with patient data for survival prediction.

Findings

Dice scores up to 0.894 for tumor segmentation

Survival prediction accuracy of 0.551 on test data

Effective use of tumor and patient features for prognosis

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.