Transformer-based heart organ segmentation using a novel axial attention and fusion mechanism

ABSTRACTRecent research on transformer-based models have highlighted particular methods for medical image segmentation. Additionally, the majority of transformer-based network designs used in computer vision applications have a significant number of parameters and demand extensive training datasets. Inspired by the success of transformers in recent researches, the unet-transformer approach has become one of the de-facto ideas in overcoming the above challenges. In this manuscript, a novel unet-transformer approach was proposed for heart image segmentation to solve parameters, limited dataset, over segmentation, sensitivity noise and higher training time problems. A framework in which a novel width and height wise axial attention mechanism is incorporated into the design to effectively give positional embeddings and encode spatial flattening. Furthermore, a novel local and global spatial attention mechanism is proposed to effectively learn the local and global interactions between encoder features. Finally, we introduce a mechanism to fuse both contexts for better feature representation and preparation into the decoder. The results demonstrate that our prototype provides a robust novel axial-attention mechanism.KEYWORDS: Transformersunetheart-segmentationlong range dependenciesspatial encodingpositional encodingaxial attentioncomputed tomography (CT) Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://www.kaggle.com/datasets/.2 https://www.synapse.org/#!Synapse:syn3193805/wiki/217752.3 https://www.synapse.org/#!Synapse:syn3193805/wiki/217789.4 https://www.creatis.insa-lyon.fr/Challenge/acdc/index.html.Additional informationFundingThis work was supported by the National Natural Science Foundation of China [grant number 61402204, 61572239 and 61772242] Research Fund for Advanced Talents of Jiangsu University [grant number 14JDG141]; Qing Lan Project; China Postdoctoral Science Foundation [grant number 2017M611737]; and Zhenjiang Social Development Project [grant number SH2016029].Notes on contributorsAddae Emmanuel AddoAddae Emmanuel Addo received the B.S. degree in computer science from the Jiangsu University, Zhenjiang city, Jiangsu province, China, in 2018, where he is currently pursuing the master's degree in computer science. His researches focus on deep learning and medical image processing.Kashala Kabe GedeonKashala Kabe Gedeon received his Ph.D. degree in Computer Science in 2021 from Jiangsu University, Zhenjiang, China. He is now a postdoctoral and lecturer at School of Computer Science and Telecommunication Engineering, Jiangsu University. Dr. Kashala has published extensively in peer reviewed journals and won several scientific awards. Currently, his research interests include medical image processing and analysis, deep learning, transfer learning and pattern recognition.Zhe LiuZhe Liu got her Ph.D. degree in Computer Science in 2012 from Jiangsu University, Zhenjiang, China. She is a visiting scholar at the Department of Radiology at the University of Pittsburgh Medical Center, Pennsylvania, USA, and also a professor at the School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang. Her research interests include image processing, data mining, and pattern recognition. She is a member of CCF and IEEE.