Post-Surgical Cavity Segmentation on MRI for Glioma Patients with Deep Learning and Transfer Learning Approach

Motivation: Currently, manual/semiautomatic methods are being used for post-surgical cavity segmentation in glioma patients, which is crucial for precise treatment planning and monitoring. Goal(s): To evaluate the potential of deep learning models for automatic post-surgical cavity segmentation. Approach: Deep learning models were trained on pre-surgery and post-surgery (within 72hours) MRI data and tested on post-surgery data. The concept of data augmentation and transfer learning were also used. Results: Initially trained on the pre-surgery BraTS’2021 challenge dataset, the models underwent further refinement with local hospital's post-surgical data, resulting in 36% improvement in post-surgical cavity segmentation (U-Net3, DSC = 0.64). Impact: This study demonstrates the potential of deep-learning in automatic segmentation of post-surgical cavity on MRI images. Proposed automatic segmentation models can assist in fast and objective treatment planning. Further optimizations for improved accuracy and validation on large dataset is required.