- 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.
- Paper Type: methodological development
- TL;DR: We propose a recurrent encoder-decoder CNN for 4D spatio-temporal deep learning, applied to multiple sclerosis lesion activity segmentation.
- Track: short paper
- Keywords: Multiple Sclerosis, Lesion Activity, Segmentation, 4D Deep Learning
- Presentation Upload: zip
- Presentation Upload Agreement: I agree that my presentation material (videos and slides) will be made public.