Background: Computer-aided diagnosis is of great significance to improve the diagnostic accuracy of pancreatic cancer that has an insidious course and does not have obvious symptoms at first. However, segmentation of pancreatic cancer is challenging because the tumors vary in size with the smallest tumor having a size of about 0.5 in diameter, and most of them have irregular shapes and unclear boundaries.
Purpose: In this study, we developed a deep learning architecture Multi-Scale Channel Attention Unet (MSCA-Unet) for pancreatic tumor segmentation and collected CT images of 419 patients from The Affiliated Hospital of Qingdao University and a public dataset. We embedded the multi-scale network into the encoder to extract semantic information at different scales and the decoder to provide supplemental information to overcome the loss of information in the upsampling and the drift of the localized tumor due to the upsampling and skip connections.
Methods: We adopted the channel attention unit after the multi-scale convolution to emphasize the informative channels, which was observed to have the effects of accelerating the positioning process, reducing false positives, and improving the accuracy of outlining very small, irregular pancreatic tumors.
Results: Our results show that our network outperformed the other current mainstream segmentation networks and achieved a Dice index of 68.03%, a Jaccard of 59.31%, and an FPR of 1.36% on the private dataset Task-01 without data pre-processing. Compared with the other pancreatic tumor segmentation networks on the public dataset Task-02, our network produced the best Dice index, 80.12%, with the assistance of the data pre-processing scheme.
Conclusions: This study strategically utilizes the multi-scale convolution and channel attention mechanism of the architecture to provide a dedicated network for segmentation of small and irregular pancreatic tumors.
Keywords: CT image; network architecture; small cancer segmentation.
© 2023 American Association of Physicists in Medicine.