Abstract
Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. Therefore, various studies have designed segmentation models based on convolutional neural networks for pancreas segmentation. However, the deep learning approach is limited by a lack of data, and studies conducted on a large computed tomography dataset are scarce. Therefore, this study aims to perform deep-learning-based semantic segmentation on 1006 participants and evaluate the automatic segmentation performance of the pancreas via four individual three-dimensional segmentation networks. In this study, we performed internal validation with 1,006 patients and external validation using the cancer imaging archive pancreas dataset. We obtained mean precision, recall, and dice similarity coefficients of 0.869, 0.842, and 0.842, respectively, for internal validation via a relevant approach among the four deep learning networks. Using the external dataset, the deep learning network achieved mean precision, recall, and dice similarity coefficients of 0.779, 0.749, and 0.735, respectively. We expect that generalized deep-learning-based systems can assist clinical decisions by providing accurate pancreatic segmentation and quantitative information of the pancreas for abdominal computed tomography.
Highlights
Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis
Cystic tumors that are inadvertently identified in the pancreas require continuous follow-up[2, 3]. This is typically followed by computed tomography (CT) scans of the abdomen to observe the increase in lesion size
This study presents an automated deep learning method for pancreatic segmentation and volumetry using the abdominal CT images of 1006 participants who underwent a health checkup
Summary
Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. We expect that generalized deep-learning-based systems can assist clinical decisions by providing accurate pancreatic segmentation and quantitative information of the pancreas for abdominal computed tomography. Cystic tumors that are inadvertently identified in the pancreas require continuous follow-up[2, 3] This is typically followed by computed tomography (CT) scans of the abdomen to observe the increase in lesion size. Obtaining the volume of the pancreas from abdominal CT scans based on artificial intelligence can assist in calculating the quantitative pancreatic volume and the patient’s endocrine and exocrine functions, which enables a more scientific and objective treatment for the patients. The current study develops a technique for calculating the volume of the pancreas based on deep learning technology using abdominal CT scan images.
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