# Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma

arXiv: https://arxiv.org/abs/2208.04680 · HTML: https://mlsanity.com/paper/2208.04680/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018)
- [CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2201.02831/) (2022)
- [Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks](https://mlsanity.com/paper/1709.00382/) (2017)
- [Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation](https://mlsanity.com/paper/1902.08128/) (2019)

## Built on by (most cited first, 4 of 4)

- [Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation](https://mlsanity.com/paper/2311.12437/) (2023)
- [A 3D Multi-Style Cross-Modality Segmentation Framework for Segmenting Vestibular Schwannoma and Cochlea](https://mlsanity.com/paper/2311.11578/) (2023)
- [Fine-Grained Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation](https://mlsanity.com/paper/2311.15090/) (2023)
- [A Clinical Guideline Driven Automated Linear Feature Extraction for Vestibular Schwannoma](https://mlsanity.com/paper/2310.19392/) (2023)

## Abstract (arXiv)

Vestibular Schwannoma (VS) typically grows from the inner ear to the brain. It can be separated into two regions, intrameatal and extrameatal respectively corresponding to being inside or outside the inner ear canal. The growth of the extrameatal regions is a key factor that determines the disease management followed by the clinicians. In this work, a VS segmentation approach with subdivision into intra-/extra-meatal parts is presented. We annotated a dataset consisting of 227 T2 MRI instances, acquired longitudinally on 137 patients, excluding post-operative instances. We propose a staged approach, with the first stage performing the whole tumour segmentation and the second stage performing the intra-/extra-meatal segmentation using the T2 MRI along with the mask obtained from the first stage. To improve on the accuracy of the predicted meatal boundary, we introduce a task-specific loss which we call Boundary Distance Loss. The performance is evaluated in contrast to the direct intrameatal extrameatal segmentation task performance, i.e. the Baseline. Our proposed method, with the two-stage approach and the Boundary Distance Loss, achieved a Dice score of 0.8279+-0.2050 and 0.7744+-0.1352 for extrameatal and intrameatal regions respectively, significantly improving over the Baseline, which gave Dice score of 0.7939+-0.2325 and 0.7475+-0.1346 for the extrameatal and intrameatal regions respectively.