Fast meningioma segmentation in T1-weighted MRI volumes using a lightweight 3D deep learning architecture

• URL: http://arxiv.org/abs/2010.07002v1
• Date: Wed, 14 Oct 2020 12:26:53 GMT
• Title: Fast meningioma segmentation in T1-weighted MRI volumes using a lightweight 3D deep learning architecture
• Authors: David Bouget, Andr\'e Pedersen, Sayied Abdol Mohieb Hosainey, Johanna Vanel, Ole Solheim, Ingerid Reinertsen
• Abstract summary: We optimized the segmentation and processing speed performances using a large number of surgically treated meningiomas and untreated meningiomas. We studied two different 3D neural network architectures: (i) a simple encoder-decoder similar to a 3D U-Net, and (ii) a lightweight multi-scale architecture (PLS-Net) The models were evaluated in terms of detection accuracy, segmentation accuracy and training/inference speed.
• Score: 0.19573380763700712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic and consistent meningioma segmentation in T1-weighted MRI volumes and corresponding volumetric assessment is of use for diagnosis, treatment planning, and tumor growth evaluation. In this paper, we optimized the segmentation and processing speed performances using a large number of both surgically treated meningiomas and untreated meningiomas followed at the outpatient clinic. We studied two different 3D neural network architectures: (i) a simple encoder-decoder similar to a 3D U-Net, and (ii) a lightweight multi-scale architecture (PLS-Net). In addition, we studied the impact of different training schemes. For the validation studies, we used 698 T1-weighted MR volumes from St. Olav University Hospital, Trondheim, Norway. The models were evaluated in terms of detection accuracy, segmentation accuracy and training/inference speed. While both architectures reached a similar Dice score of 70% on average, the PLS-Net was more accurate with an F1-score of up to 88%. The highest accuracy was achieved for the largest meningiomas. Speed-wise, the PLS-Net architecture tended to converge in about 50 hours while 130 hours were necessary for U-Net. Inference with PLS-Net takes less than a second on GPU and about 15 seconds on CPU. Overall, with the use of mixed precision training, it was possible to train competitive segmentation models in a relatively short amount of time using the lightweight PLS-Net architecture. In the future, the focus should be brought toward the segmentation of small meningiomas (less than 2ml) to improve clinical relevance for automatic and early diagnosis as well as speed of growth estimates.

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