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Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models

Heejong Kim, Leo Milecki, Mina C Moghadam, Fengbei Liu, Minh Nguyen, Eric Qiu, Abhishek Thanki, Mert R Sabuncu

TL;DR

This study tackles the challenge of post-treatment glioma segmentation on MRI, where surgery-induced changes complicate delineation. It proposes two simple enhancements: (i) generating an additional input via subtraction image T1Gd-T1 to emphasize enhancing tissue and (ii) ensembling multiple models using STAPLE and a label-aware weighted average. Evaluated on the BraTS 2024 Post-Treatment dataset (~$2{,}200$ patients across seven centers) with four MRI modalities (T1, T1Gd, T2, FLAIR) and a resection cavity label, the approach demonstrates improvements in lesion-wise metrics across tumor sub-regions ET, NETC, SNFH, and RC, with notable gains for RC and WT when ensembles are used. The findings highlight that simple data fusion and ensemble strategies can meaningfully improve automatic post-treatment glioma segmentation and offer a practical path toward more reliable monitoring in clinical workflows, with potential applicability to other segmentation tasks in medical imaging.

Abstract

Segmentation is a crucial task in the medical imaging field and is often an important primary step or even a prerequisite to the analysis of medical volumes. Yet treatments such as surgery complicate the accurate delineation of regions of interest. The BraTS Post-Treatment 2024 Challenge published the first public dataset for post-surgery glioma segmentation and addresses the aforementioned issue by fostering the development of automated segmentation tools for glioma in MRI data. In this effort, we propose two straightforward approaches to enhance the segmentation performances of deep learning-based methodologies. First, we incorporate an additional input based on a simple linear combination of the available MRI sequences input, which highlights enhancing tumors. Second, we employ various ensembling methods to weigh the contribution of a battery of models. Our results demonstrate that these approaches significantly improve segmentation performance compared to baseline models, underscoring the effectiveness of these simple approaches in improving medical image segmentation tasks.

Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models

TL;DR

This study tackles the challenge of post-treatment glioma segmentation on MRI, where surgery-induced changes complicate delineation. It proposes two simple enhancements: (i) generating an additional input via subtraction image T1Gd-T1 to emphasize enhancing tissue and (ii) ensembling multiple models using STAPLE and a label-aware weighted average. Evaluated on the BraTS 2024 Post-Treatment dataset (~ patients across seven centers) with four MRI modalities (T1, T1Gd, T2, FLAIR) and a resection cavity label, the approach demonstrates improvements in lesion-wise metrics across tumor sub-regions ET, NETC, SNFH, and RC, with notable gains for RC and WT when ensembles are used. The findings highlight that simple data fusion and ensemble strategies can meaningfully improve automatic post-treatment glioma segmentation and offer a practical path toward more reliable monitoring in clinical workflows, with potential applicability to other segmentation tasks in medical imaging.

Abstract

Segmentation is a crucial task in the medical imaging field and is often an important primary step or even a prerequisite to the analysis of medical volumes. Yet treatments such as surgery complicate the accurate delineation of regions of interest. The BraTS Post-Treatment 2024 Challenge published the first public dataset for post-surgery glioma segmentation and addresses the aforementioned issue by fostering the development of automated segmentation tools for glioma in MRI data. In this effort, we propose two straightforward approaches to enhance the segmentation performances of deep learning-based methodologies. First, we incorporate an additional input based on a simple linear combination of the available MRI sequences input, which highlights enhancing tumors. Second, we employ various ensembling methods to weigh the contribution of a battery of models. Our results demonstrate that these approaches significantly improve segmentation performance compared to baseline models, underscoring the effectiveness of these simple approaches in improving medical image segmentation tasks.
Paper Structure (13 sections, 3 figures, 4 tables)

This paper contains 13 sections, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Dataset
  4. Additional Input Modality (T1Gd-T1)
  5. Architecture
  6. nnUNet:
  7. nnUNet ResEnc:
  8. SegResNet:
  9. Training
  10. Test time augmentation
  11. Ensemble
  12. Results
  13. Discussion

Figures (3)

  • Figure 1: Four MR imaging modalities from the 2024 BraTS challenge dataset (A-D) and a calculated modality (T1Gd-T1, E). Tumor sub-region labels (F) include enhancing tissue (ET, blue), non-enhancing tumor core (NETC, red), surrounding non-enhancing FLAIR hyperintensity (SNFH, green), and resection cavity (RC, yellow).
  • Figure 2: Segmentation results visualization on one subject in the internal validation set on the different MR imaging input modalities (lines). The ground truth annotation (GT) is compared against our baseline models (columns #1 to #4) and our models with the T1Gd-T1 input (columns #5 to #8). Labels include enhancing tissue (ET, blue), non-enhancing tumor core (NETC, red), surrounding non-enhancing FLAIR hyperintensity (SNFH, green), and resection cavity (RC, yellow).
  • Figure 3: Segmentation results visualization on one subject in the internal validation set on the different MR imaging input modalities (lines). The ground truth annotation (GT) is compared against our ensemble models using STAPLE (columns #9 to #11) and the proposed weighted approach (columns #12 to #14). Labels include enhancing tissue (ET, blue), non-enhancing tumor core (NETC, red), surrounding non-enhancing FLAIR hyperintensity (SNFH, green), and resection cavity (RC, yellow).