Deep learning based neural network application for automatic ultrasonic computed tomographic bone image segmentation

Fradi Marwa; El-Hadi Zahzah; Kais Bouallegue; Mohsen Machhout

doi:10.1007/s11042-022-12322-3

Deep learning based neural network application for automatic ultrasonic computed tomographic bone image segmentation

Multimed Tools Appl. 2022;81(10):13537-13562. doi: 10.1007/s11042-022-12322-3. Epub 2022 Feb 16.

Authors

Fradi Marwa^{1

2}, El-Hadi Zahzah², Kais Bouallegue³, Mohsen Machhout¹

Affiliations

¹ Physic Department of Faculty of Sciences of Monastir, Monastir University, Monastir, Tunisia.
² Laboratory of Informatics, Image and Interaction (L3i, France), La Rochelle University, La Rochelle, France.
³ ISSAT Sousse, Sousse University, Sousse, Tunisia.

Abstract

Deep-learning techniques have led to technological progress in the area of medical imaging segmentation especially in the ultrasound domain. In this paper, the main goal of this study is to optimize a deep-learning-based neural network architecture for automatic segmentation in Ultrasonic Computed Tomography (USCT) bone images in a short time process. The proposed method is based on an end to end neural network architecture. First, the novelty is shown by the improvement of Variable Structure Model of Neuron (VSMN), which is trained for both USCT noise removal and dataset augmentation. Second, a VGG-SegNet neural network architecture is trained and tested on new USCT images not seen before for automatic bone segmentation. Therefore, we offer a free USCT dataset. In addition, the proposed model is implemented on both the CPU and the GPU, hence overcoming previous works by a value of 97.38% and 96% for training and validation and achieving high segmentation accuracy for testing with a small error of 0.006, in a short time process. The suggested method demonstrates its ability to augment USCT data and then to automatically segment USCT bone structures achieving excellent accuracy outperforming the state of the art.

Keywords: GPU; Segmentation; Time process; USCT; VSMN-VGG-SegNet.