ITM Web Conf.
Volume 54, 20232nd International Conference on Advances in Computing, Communication and Security (I3CS-2023)
|Number of page(s)||9|
|Published online||04 July 2023|
Self-supervised approach for organs at risk segmentation of abdominal CT images
1 National Institute of Technology Calicut, Kerala, India
2 Jyothi Engineering College, Cheruthuruthy, Kerala, India
3 MVR Cancer Centre & Research Institute, Calicut, India
Accurate segmentation of organs at risk is essential for radiation therapy planning. However, manual segmentation is time-consuming and prone to inter and intra-observer variability. This study proposes a self-supervision based attention UNet model for OAR segmentation of abdominal CT images. The model utilizes a self-supervision mechanism to train itself without the need for manual annotations. The attention mechanism is used to highlight important features and suppress irrelevant ones, thus improving the model’s accuracy. The model is evaluated on a dataset of 100 abdominal CT scans and compared its perfor mance with state-of-the-art methods. Our results show that the proposed model got comparable performance in terms of the dice similarity coefficient. More over, the inference time is much faster than traditional manual segmentation methods, making it a promising tool for clinical use.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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