Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training

TL;DR

This paper presents an advanced 3D brain tumor segmentation method using SegResNet trained with an automatic multi-precision approach, achieving high dice scores for different tumor regions.

Contribution

It introduces a novel training approach with assorted precision for 3D segmentation, improving accuracy over existing methods.

Findings

Dice score of 0.84 for tumor core

Dice score of 0.90 for whole tumor

Dice score of 0.79 for enhanced tumor

Abstract

A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of non-essential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, and sensory changes. This research explores two main categories of brain tumors: benign and malignant. Benign spreads steadily, and malignant expresses growth, making it dangerous. Early identification of brain tumors is a crucial factor for the survival of patients. This research provides a state-of-the-art approach to the early identification of tumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for three-dimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the dice loss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got a dice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.