@misc{10481/91757,
year = {2024},
month = {3},
url = {https://hdl.handle.net/10481/91757},
abstract = {Cancer is one of the leading causes of death in the world, with radiotherapy as one of the treatment options. Radiotherapy planning starts with delineating the affected area from healthy organs, called organs at risk (OAR). A new approach to automatic OAR segmentation in the chest cavity in Computed Tomography (CT) images is presented. The proposed approach is based on the modified U-Net architecture with the ResNet-34 encoder, which is the baseline adopted in this work. The new two-branch CS-SA U-Net architecture is proposed, which consists of two parallel U-Net models in which self-attention blocks with cosine similarity as query-key similarity function (CS-SA) blocks are inserted between the encoder and decoder, which enabled the use of consistency regularisation. The proposed solution demonstrates state-of-the-art performance for the problem of OAR segmentation in CT images on the publicly available SegTHOR benchmark dataset in terms of a Dice coefficient (oesophagus—0.8714, heart—0.9516, trachea—0.9286, aorta—0.9510) and Hausdorff distance (oesophagus—0.2541, heart—0.1514, trachea—0.1722, aorta—0.1114) and significantly outperforms the baseline. The current approach is demonstrated to be viable for improving the quality of OAR segmentation for radiotherapy planning.},
organization = {PID2022‐137451OB‐I00 and PID2022‐137629OA‐I00 projects funded by the MICIU/AEIAEI/10.13039/501100011033 and by ERDF/EU},
publisher = {Wiley},
title = {Improved organs at risk segmentation based on modified U‐Net with self‐attention and consistency regularisation},
doi = {10.1049/cit2.12303},
author = {Manko, Maksym and Popov, Anton and Gorriz Sáez, Juan Manuel and Ramírez Pérez De Inestrosa, Javier},
}