Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks

TL;DR

This paper presents an ensemble CNN-based approach for tumor segmentation in medical images, specifically targeting BraTS 2023 adult glioma and pediatric tumor tasks, achieving high accuracy and robustness.

Contribution

It introduces a novel ensemble method combining SegResNet and MedNeXt models with postprocessing techniques for improved tumor segmentation in BraTS 2023 challenge.

Findings

Achieved third place in BraTS 2023 adult glioma segmentation challenge.

Attained an average Dice score of 0.8313 on the test set.

Demonstrated improved segmentation accuracy with robust postprocessing.

Abstract

Automated segmentation proves to be a valuable tool in precisely detecting tumors within medical images. The accurate identification and segmentation of tumor types hold paramount importance in diagnosing, monitoring, and treating highly fatal brain tumors. The BraTS challenge serves as a platform for researchers to tackle this issue by participating in open challenges focused on tumor segmentation. This study outlines our methodology for segmenting tumors in the context of two distinct tasks from the BraTS 2023 challenge: Adult Glioma and Pediatric Tumors. Our approach leverages two encoder-decoder-based CNN models, namely SegResNet and MedNeXt, for segmenting three distinct subregions of tumors. We further introduce a set of robust postprocessing to improve the segmentation, especially for the newly introduced BraTS 2023 metrics. The specifics of our approach and comprehensive performance analyses are expounded upon in this work. Our proposed approach achieves third place in the BraTS 2023 Adult Glioma Segmentation Challenges with an average of 0.8313 and 36.38 Dice and HD95 scores on the test set, respectively.