# Improving the Generalizability of Deep Learning for T2-Lesion Segmentation of Gliomas in the Post-Treatment Setting

## TL;DR

This paper improves deep learning models for tracking brain tumor changes after treatment by using mixed data and new techniques to better segment T2-lesions in MRI scans.

## Contribution

The study introduces data mixing, transfer learning, and spatial regularization to enhance T2-lesion segmentation in post-treatment glioma MRI scans.

## Key findings

- Including 26% treated patients in training improved performance by 13.9%.
- Fine-tuning with treated glioma data improved sensitivity by 2.5% compared to data mixing.
- Spatial regularization improved performance metrics like HD, Dice, and sensitivity when used with transfer learning.

## Abstract

Although fully automated volumetric approaches for monitoring brain tumor response have many advantages, most available deep learning models are optimized for highly curated, multi-contrast MRI from newly diagnosed gliomas, which are not representative of post-treatment cases in the clinic. Improving segmentation for treated patients is critical to accurately tracking changes in response to therapy. We investigated mixing data from newly diagnosed (n = 208) and treated (n = 221) gliomas in training, applying transfer learning (TL) from pre- to post-treatment imaging domains, and incorporating spatial regularization for T2-lesion segmentation using only T2 FLAIR images as input to improve generalization post-treatment. These approaches were evaluated on 24 patients suspected of progression who had received prior treatment. Including 26% of treated patients in training improved performance by 13.9%, and including more treated and untreated patients resulted in minimal changes. Fine-tuning with treated glioma improved sensitivity compared to data mixing by 2.5% (p < 0.05), and spatial regularization further improved performance when used with TL by 95th HD, Dice, and sensitivity (6.8%, 0.8%, 2.2%; p < 0.05). While training with ≥60 treated patients yielded the majority of performance gain, TL and spatial regularization further improved T2-lesion segmentation to treated gliomas using a single MR contrast and minimal processing, demonstrating clinical utility in response assessment.