|Title: 4D Deep Learning for Multiple-Sclerosis Lesion Activity Segmentation Medical Imaging with Deep Learning|
|Written by: N. Gessert and M. Bengs and J. Krüger and R. Opfer and A.-C. Ostwaldt and P. Manogaran and S. Schippling and A. Schlaefer|
|on pages: accepted|
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.