4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation

Nils Gessert; Marcel Bengs; Julia Krüger; Roland Opfer; Ann-Christin Ostwaldt; Praveena Manogaran; Sven Schippling; Alexander Schlaefer

arXiv · 2004.09216

4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation

Nils Gessert·Marcel Bengs·Julia Krüger·Roland Opfer·Ann-Christin Ostwaldt·Praveena Manogaran·Sven Schippling·Alexander Schlaefer

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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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Nils Gessert, Marcel Bengs, Julia Krüger, Roland Opfer, Ann-Christin Ostwaldt, Praveena Manogaran, Sven Schippling, Alexander Schlaefer. 2020-04-20. 4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation. https://arxiv.org/abs/2004.09216

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