4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation

• URL: http://arxiv.org/abs/2004.09216v2
• Date: Fri, 29 May 2020 19:28:54 GMT
• Title: 4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation
• Authors: Nils Gessert and Marcel Bengs and Julia Kr\"uger and Roland Opfer and Ann-Christin Ostwaldt and Praveena Manogaran and Sven Schippling and Alexander Schlaefer
• Abstract summary: We investigate whether extending this problem to full 4D deep learning using a history of MRI volumes can improve performance. We find that our proposed architecture outperforms previous approaches with a lesion-wise true positive rate of 0.84 at a lesion-wise false positive rate of 0.19.
• Score: 49.32653090178743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple sclerosis lesion activity segmentation is the task of detecting new and enlarging lesions that appeared between a baseline and a follow-up brain MRI scan. While deep learning methods for single-scan lesion segmentation are common, deep learning approaches for lesion activity have only been proposed recently. Here, a two-path architecture processes two 3D MRI volumes from two time points. In this work, we investigate whether extending this problem to full 4D deep learning using a history of MRI volumes and thus an extended baseline can improve performance. For this purpose, we design a recurrent multi-encoder-decoder architecture for processing 4D data. We find that adding more temporal information is beneficial and our proposed architecture outperforms previous approaches with a lesion-wise true positive rate of 0.84 at a lesion-wise false positive rate of 0.19.

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