Relative location prediction in CT scan images using convolutional neural networks

• Published in: Computer methods and programs in biomedicine Vol. 160; pp. 43 - 49
• Main Authors: Guo, Jiajia, Du, Hongwei, Zhu, Jianyue, Yan, Ting, Qiu, Bensheng
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.07.2018
• Subjects: Convolutional neural networks; CT scan images; Relative location prediction; Relative location prediction; Convolutional neural networks; CT scan images
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2018.03.025

•It is a regression model based on 1D-CNN for predicting the relative location of CT scan images.•A public dataset in the UCI repository was used to evaluate the proposed model.•The accuracy and speed of the proposed model outperformed other machine learning-based methods by a large margin.•The performance of the proposed model on small datasets was also better than that of the KNN model. Relative location prediction in computed tomography (CT) scan images is a challenging problem. Many traditional machine learning methods have been applied in attempts to alleviate this problem. However, the accuracy and speed of these methods cannot meet the requirement of medical scenario. In this paper, we propose a regression model based on one-dimensional convolutional neural networks (CNN) to determine the relative location of a CT scan image both quickly and precisely. In contrast to other common CNN models that use a two-dimensional image as an input, the input of this CNN model is a feature vector extracted by a shape context algorithm with spatial correlation. Normalization via z-score is first applied as a pre-processing step. Then, in order to prevent overfitting and improve model's performance, 20% of the elements of the feature vectors are randomly set to zero. This CNN model consists primarily of three one-dimensional convolutional layers, three dropout layers and two fully-connected layers with appropriate loss functions. A public dataset is employed to validate the performance of the proposed model using a 5-fold cross validation. Experimental results demonstrate an excellent performance of the proposed model when compared with contemporary techniques, achieving a median absolute error of 1.04 cm and mean absolute error of 1.69 cm. The time taken for each relative location prediction is approximately 2 ms. Results indicate that the proposed CNN method can contribute to a quick and accurate relative location prediction in CT scan images, which can improve efficiency of the medical picture archiving and communication system in the future.