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

by   David Bouget, et al.

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 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.


page 3

page 5

page 8

page 11

page 12


Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms

Meningiomas are the most common type of primary brain tumor, accounting ...

FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation

This contribution presents a deep learning method for the segmentation o...

USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

Prostate cancer is the most common malignant tumors in men but prostate ...

3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI

Brain tumor segmentation plays a pivotal role in medical image processin...

A new smart-cropping pipeline for prostate segmentation using deep learning networks

Prostate segmentation from magnetic resonance imaging (MRI) is a challen...

Dilated deeply supervised networks for hippocampus segmentation in MRI

Tissue loss in the hippocampi has been heavily correlated with the progr...

Please sign up or login with your details

Forgot password? Click here to reset