Spinal Metastases Segmentation in MR Imaging using Deep Convolutional Neural Networks

• URL: http://arxiv.org/abs/2001.05834v2
• Date: Tue, 28 Jan 2020 10:21:08 GMT
• Title: Spinal Metastases Segmentation in MR Imaging using Deep Convolutional Neural Networks
• Authors: Georg Hille and Johannes Steffen and Max D\"unnwald and Mathias Becker and Sylvia Saalfeld and Klaus T\"onnies
• Abstract summary: This study's objective was to segment spinal metastases in diagnostic MR images using a deep learning-based approach. We used a U-Net like architecture trained with 40 clinical cases including both, lytic and sclerotic lesion types and various MR sequences. Compared to expertly annotated lesion segmentations, the experiments yielded promising results with average Dice scores up to 77.6% and mean sensitivity rates up to 78.9%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study's objective was to segment spinal metastases in diagnostic MR images using a deep learning-based approach. Segmentation of such lesions can present a pivotal step towards enhanced therapy planning and validation, as well as intervention support during minimally invasive and image-guided surgeries like radiofrequency ablations. For this purpose, we used a U-Net like architecture trained with 40 clinical cases including both, lytic and sclerotic lesion types and various MR sequences. Our proposed method was evaluated with regards to various factors influencing the segmentation quality, e.g. the used MR sequences and the input dimension. We quantitatively assessed our experiments using Dice coefficients, sensitivity and specificity rates. Compared to expertly annotated lesion segmentations, the experiments yielded promising results with average Dice scores up to 77.6% and mean sensitivity rates up to 78.9%. To our best knowledge, our proposed study is one of the first to tackle this particular issue, which limits direct comparability with related works. In respect to similar deep learning-based lesion segmentations, e.g. in liver MR images or spinal CT images, our experiments showed similar or in some respects superior segmentation quality. Overall, our automatic approach can provide almost expert-like segmentation accuracy in this challenging and ambitious task.

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