# Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modality Domain Adaptation

arXiv: https://arxiv.org/abs/2303.07674 · HTML: https://mlsanity.com/paper/2303.07674/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation](https://mlsanity.com/paper/2002.02255/) (2020)
- [CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2201.02831/) (2022)
- [Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation](https://mlsanity.com/paper/1907.13590/) (2019)
- [Scribble-based Domain Adaptation via Co-segmentation](https://mlsanity.com/paper/2007.03632/) (2020)
- [Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss](https://mlsanity.com/paper/1906.03906/) (2019)
- [COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training](https://mlsanity.com/paper/2203.16557/) (2022)
- [Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training](https://mlsanity.com/paper/2109.14219/) (2021)

## Abstract (arXiv)

The Koos grading scale is a classification system for vestibular schwannoma (VS) used to characterize the tumor and its effects on adjacent brain structures. The Koos classification captures many of the characteristics of treatment deci-sions and is often used to determine treatment plans. Although both contrast-enhanced T1 (ceT1) scanning and high-resolution T2 (hrT2) scanning can be used for Koos Classification, hrT2 scanning is gaining interest because of its higher safety and cost-effectiveness. However, in the absence of annotations for hrT2 scans, deep learning methods often inevitably suffer from performance deg-radation due to unsupervised learning. If ceT1 scans and their annotations can be used for unsupervised learning of hrT2 scans, the performance of Koos classifi-cation using unlabeled hrT2 scans will be greatly improved. In this regard, we propose an unsupervised cross-modality domain adaptation method based on im-age translation by transforming annotated ceT1 scans into hrT2 modality and us-ing their annotations to achieve supervised learning of hrT2 modality. Then, the VS and 7 adjacent brain structures related to Koos classification in hrT2 scans were segmented. Finally, handcrafted features are extracted from the segmenta-tion results, and Koos grade is classified using a random forest classifier. The proposed method received rank 1 on the Koos classification task of the Cross-Modality Domain Adaptation (crossMoDA 2022) challenge, with Macro-Averaged Mean Absolute Error (MA-MAE) of 0.2148 for the validation set and 0.26 for the test set.