International journal of computer assisted radiology and surgery · 2026
Automatic MRI-Based segmentation of ablation zones in spine and liver: A comparative study of network performance and influencing factors.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Rothert Janine, Saalfeld Sylvia, Gutberlet Marcel, Wacker Frank, Düx Daniel, Hensen Bennet, Becker Mathias, Rose Georg, Hille Georg
Аннотация
PURPOSE: Ablation therapies are a treatment option for cancer patients, particularly for conditions such as spinal metastases and liver tumors. Precisely delineating ablation zones is essential for accurately assessing treatment success. However, research in MRI-guided interventions remains limited for automated segmentation approaches and quantitative analysis.
METHODS: We developed a framework for automated segmentation of ablation zones following thermal interventions. The performance was tested on two representative types of clinical cases from different clinical sites: post-ablative liver lesions and spinal metastases. Four leading neural networks (nnUNet, TransUNet, SwinUNETR, and SwinUNETR-V2) were evaluated for their segmentation accuracy in segmenting necrotic tissue. Additionally, a statistical analysis was performed to investigate the influence of an optimized image ROI selection on the segmentation performance.
RESULTS: The nnUNet achieved the highest segmentation performance, with a Dice Similarity Coefficient of 83.3 ± 13.2% for spinal metastases and 82.2 ± 12.4% for liver lesions. The statistical analysis revealed that cropping images to a standardized ROI size provided the optimal balance between user interaction and networks' performance.
CONCLUSION: The automated segmentation accuracies are comparable to the inter-rater variability observed among radiologists for spinal imaging and establish the new state-of-the-art for liver MRIs, indicating the potential to facilitate the clinical routine for interventions.
METHODS: We developed a framework for automated segmentation of ablation zones following thermal interventions. The performance was tested on two representative types of clinical cases from different clinical sites: post-ablative liver lesions and spinal metastases. Four leading neural networks (nnUNet, TransUNet, SwinUNETR, and SwinUNETR-V2) were evaluated for their segmentation accuracy in segmenting necrotic tissue. Additionally, a statistical analysis was performed to investigate the influence of an optimized image ROI selection on the segmentation performance.
RESULTS: The nnUNet achieved the highest segmentation performance, with a Dice Similarity Coefficient of 83.3 ± 13.2% for spinal metastases and 82.2 ± 12.4% for liver lesions. The statistical analysis revealed that cropping images to a standardized ROI size provided the optimal balance between user interaction and networks' performance.
CONCLUSION: The automated segmentation accuracies are comparable to the inter-rater variability observed among radiologists for spinal imaging and establish the new state-of-the-art for liver MRIs, indicating the potential to facilitate the clinical routine for interventions.