Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training
2026-05-05 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionMachine Learning
AI summaryⓘ
The authors studied brain tumors, which are abnormal growths in the brain that can be harmless (benign) or dangerous (malignant). They used a special computer method called SegResNet to help find tumors early by analyzing 3D brain images. Their method works well and was measured using a score called the dice score, showing good accuracy in identifying different parts of tumors. Early detection like this can help doctors treat brain tumors better.
brain tumorbenign tumormalignant tumorSegResNet3D segmentationdice lossdice scoretumor corewhole tumorenhanced tumor
Authors
Adwaitt Pandya, Ozioma C. Oguine, Harita Bhargava, Shrikant Zade
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.