Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks. (March 2019)

• Record Type: Journal Article
• Authors: Diniz, João Otávio Bandeira; Diniz, Pedro Henrique Bandeira; Valente, Thales Levi Azevedo; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso
• Is Part Of: Computer methods and programs in biomedicine. Volume 170(2019)
• Page Start: 53
• Page End: 67
• Publication Date: 2019-03
• Subjects: Computer-aided detection -- Convolutional neural network -- Planning CT -- IMSLIC -- Medical images -- Spinal cord; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.01.005
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This work investigates computational method for automatic detection of spinal cord in planning CT scan for radiotherapy. The spinal cord was detected in CT scan using adaptive template matching, IMSLIC and convolutional neural network. The method was applied with 36 CT scans with an average of 200 slices. The method uses image processing and deep learning techniques. The method achieved an accuracy of 92.55%, specificity of 92.87% and sensitivity of 89.23% with 0.065 of false positives per images. Abstract: Background and Objective: The spinal cord is a very important organ that must be protected in treatments of radiotherapy (RT), considered an organ at risk (OAR). Excess rays associated with the spinal cord can cause irreversible diseases in patients who are undergoing radiotherapy. For the planning of treatments with RT, computed tomography (CT) scans are commonly used to delimit the OARs and to analyze the impact of rays in these organs. Delimiting these OARs take a lot of time from medical specialists, plus the fact that involves a large team of professionals. Moreover, this task made slice-by-slice becomes an exhaustive and consequently subject to errors, especially in organs such as the spinal cord, which extend through several slices of the CT and requires precise segmentation. Thus, we propose, in this work, a computational methodology capable of detecting spinal cord in planning CT images. Methods: The techniques highlighted in this methodology are adaptive template matching for initial segmentation, intrinsic manifold simple linear iterative clustering (IMSLIC) for candidate segmentation and convolutional neural networks (CNN) for candidate classification, that consists of four steps: (1) images acquisition, (2) initial segmentation, (3) candidates segmentation and (4) candidates classification. Results: The methodology was applied on 36 planning CT images provided by The Cancer Imaging Archive, and achieved an accuracy of 92.55%, specificity of 92.87% and sensitivity of 89.23% with 0.065 of false positives per images, without any false positives reduction technique, in detection of spinal cord. Conclusions: It is demonstrated the feasibility of the analysis of planning CT images using IMSLIC and convolutional neural network techniques to achieve success in detection of spinal cord regions.