Abstract
Combinations of data augmentation methods and deep learning architectures for automatic pancreas segmentation on CT images are proposed and evaluated. Images from a public CT dataset of pancreas segmentation were used to evaluate the models. Baseline U-net and deep U-net were chosen for the deep learning models of pancreas segmentation. Methods of data augmentation included conventional methods, mixup, and random image cropping and patching (RICAP). Ten combinations of the deep learning models and the data augmentation methods were evaluated. Four-fold cross validation was performed to train and evaluate these models with data augmentation methods. The dice similarity coefficient (DSC) was calculated between automatic segmentation results and manually annotated labels and these were visually assessed by two radiologists. The performance of the deep U-net was better than that of the baseline U-net with mean DSC of 0.703–0.789 and 0.686–0.748, respectively. In both baseline U-net and deep U-net, the methods with data augmentation performed better than methods with no data augmentation, and mixup and RICAP were more useful than the conventional method. The best mean DSC was obtained using a combination of deep U-net, mixup, and RICAP, and the two radiologists scored the results from this model as good or perfect in 76 and 74 of the 82 cases.
Highlights
Identification of anatomical structures is a fundamental step for radiologists in the interpretation of medical images
An estimated 606,880 Americans were predicted to die from cancer in 2019, in which 45,750 deaths would be due to pancreatic cancer [6]
One of the reasons for this low survival rate is the difficulty in the detection of pancreatic cancer in its early stages, because the organ is located in the retroperitoneal space and is in close proximity to other organs
Summary
Identification of anatomical structures is a fundamental step for radiologists in the interpretation of medical images. Automatic and accurate organ identification or segmentation is important for medical image analysis, computer-aided detection, and computer-aided diagnosis. Many studies have worked on automatic and accurate segmentation of organs, including lung, liver, pancreas, uterus, and muscle [1,2,3,4,5]. One of the reasons for this low survival rate is the difficulty in the detection of pancreatic cancer in its early stages, because the organ is located in the retroperitoneal space and is in close proximity to other organs. Computer-aided detection and/or diagnosis using computed tomography (CT) may contribute to a reduction in the number of deaths caused by pancreatic cancer, similar to the effect of CT screenings on lung cancer [7,8]. Accurate segmentation of pancreas is the first step in the computer-aided detection/diagnosis system of pancreatic cancer
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