Development of a convolutional neural network for the identification and the measurement of the median nerve on ultrasound images acquired at carpal tunnel level

Abstract

Background: Deep learning applied to ultrasound (US) can provide a feedback to the sonographer about the correct identification of scanned tissues and allows for faster and standardized measurements. The most frequently adopted parameter for US diagnosis of carpal tunnel syndrome is the increasing of the cross-sectional area (CSA) of the median nerve. Our aim was to develop a deep learning algorithm, relying on convolutional neural networks (CNNs), for the localization and segmentation of the median nerve and the automatic measurement of its CSA on US images acquired at the proximal inlet of the carpal tunnel.

Methods: Consecutive patients with rheumatic and musculoskeletal disorders were recruited. Transverse US images were acquired at the carpal tunnel inlet, and the CSA was manually measured. Anatomical variants were registered. The dataset consisted of 246 images (157 for training, 40 for validation, and 49 for testing) from 103 patients each associated with manual annotations of the nerve boundary. A Mask R-CNN, state-of-the-art CNN for image semantic segmentation, was trained on this dataset to accurately localize and segment the median nerve section. To evaluate the performances on the testing set, precision (Prec), recall (Rec), mean average precision (mAP), and Dice similarity coefficient (DSC) were computed. A sub-analysis excluding anatomical variants was performed. The CSA was automatically measured by the algorithm.

Results: The algorithm correctly identified the median nerve in 41/49 images (83.7%) and in 41/43 images (95.3%) excluding anatomical variants. The following metrics were obtained (with and without anatomical variants, respectively): Prec 0.86 ± 0.33 and 0.96 ± 0.18, Rec 0.88 ± 0.33 and 0.98 ± 0.15, mAP 0.88 ± 0.33 and 0.98 ± 0.15, and DSC 0.86 ± 0.19 and 0.88 ± 0.19. The agreement between the algorithm and the sonographer CSA measurements was excellent [ICC 0.97 (0.94-0.98)].

Conclusions: The developed algorithm has shown excellent performances, especially if excluding anatomical variants. Future research should aim at expanding the US image dataset including a wider spectrum of normal anatomy and pathology. This deep learning approach has shown very high potentiality for a fully automatic support for US assessment of carpal tunnel syndrome.

Fig. 1

Mask R-CNN is a CNN made of backbone (composed by a ResNet101 and a feature pyramid network (FPN)), a region proposal network (RPN), ROIAlign, and three heads, for classification, bounding-box regression, and segmentation

Fig. 2

Correct localization and segmentation of the median nerve. Transverse scans acquired at the carpal tunnel proximal inlet in two patients (A-A′ and B-B′) showing in the left panels (A and B) the manual annotations of the boundary of the median nerve made by the operator (arrows) and in the right panels (A′ and B′) the corresponding predictions made by the algorithm (open arrows). p, pisiform bone

Fig. 3

Representative images of incorrect predictions. Transverse scans acquired at the carpal tunnel proximal inlet in two patients showing the correct identification of only one branch (open arrow) of a bifid median nerve (arrows) (A-A′) and the wrong inclusion of an adjacent vessel (arrowhead) in the prediction of the median nerve (asterisk) (B-B′). p, pisiform bone