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For rectal cancer, automatic tumor segmentation from MRI remains difficult due to confounding surrounding organs with similar shapes and insufficient high-resolution features in conventional deep networks. 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BMC Medical Imaging (2024) 24:95 [https://doi.org/10.1186/s12880-024-01269-6](https://doi.org/10.1186/s12880-024-01269-6)  \nBMC Medical Imaging  \n RESEARCH Open Access  \nImaging segmentation mechanism for rectal tumors using improved U-Net  \nKenan Zhang2,3, Xiaotang Yang5*, Yanfen Cui5, Jumin Zhao2,3,4 and Dengao Li1,3,4*  \nAbstract  \nObjective In radiation therapy, cancerous region segmentation in magnetic resonance images (MRI) is a critical step. For rectal cancer, the automatic segmentation of rectal tumors from an MRI is a great challenge. There are two main shortcomings in existing deep learning-based methods that lead to incorrect segmentation: 1) there are many organs surrounding the rectum, and the shape of some organs is similar to that of rectal tumors; 2) high-level features extracted by conventional neural networks often do not contain enough high-resolution information. Therefore, an improved U-Net segmentation network based on attention mechanisms is proposed to replace the traditional U-Net network.  \nMethods The overall framework of the proposed method is based on traditional U-Net. A ResNeSt module was added to extract the overall features, and a shape module was added after the encoder layer. We then combined the outputs ofthe shape module and the decoder to obtain the results. Moreover, the model used different types of attention mechanisms, so that the network learned information to improve segmentation accuracy.  \nResults We validated the effectiveness of the proposed method using 3773 2D MRI datasets from 304 patients. The results showed that the proposed method achieved 0. 987, 0 . 946, 0 . 897, and 0.899 for Dice, MPA, MioU, and FWIoU, respectively; these values are significantly better than those of other existing methods.  \nConclusion Due to time savings, the proposed method can help radiologists segment rectal tumors effectively and enable them to focus on patients whose cancerous regions are difficult for the network to segment.  \nSignificance The proposed method can help doctors segment rectal tumors, thereby ensuring good diagnostic quality and accuracy.  \nKeywords Semantic segmentation, U-Net, Rectal cancer, MR image  \n*Correspondence: Xiaotang Yang [yangxt210@126.com](yangxt210@126.com)[ ](yangxt210@126.com)Dengao Li[lidengao@tyut.edu.cn](lidengao@tyut.edu.cn)  \n1 College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China  \n2 College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan 030024, China  \n3 Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan 030024, China  \n4 Intelligent Perception Engineering Technology Center of Shanxi, Taiyuan 030024, China  \n5 Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan 030013, China  \nIntroduction  \nCancer is a worldwide problem that leads to death [1]. Rectal cancer [2] is one of the most common malignant cancers of the digestive tract. According to the GLOBOCAN 2020 Cancer Incidence and Mortality Assessment published by the International Agency for Research on Cancer [3], the incidence of rectal cancer ranks third of all kinds of cancers, and its mortality ranks second. With the impact of the aging population and unhealthy diet, rectal cancer tends to have the highest incidence and diagnostic rates. Generally, the period from normal intestinal tissue to canceration is very long, approximately  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Com","cbCaipZpXgcYrZU4","https://ap.wps.com/l/cbCaipZpXgcYrZU4","pdf",2788850,"English","# Abstract\n## Objective\n## Methods\n## Results\n## Conclusion\n## Significance\n# Introduction\n## Background and clinical need\n## Imaging modalities and MRI role\n## Challenges in automatic segmentation","[{\"question\":\"Why is automatic rectal tumor segmentation from MRI challenging?\",\"answer\":\"Surrounding organs can resemble the rectal tumor shapes, and conventional neural networks may not extract enough high-resolution features for accurate segmentation.\"},{\"question\":\"What is the core idea of the proposed method?\",\"answer\":\"It improves the traditional U-Net by incorporating a ResNeSt module for overall feature extraction and a shape module after the encoder, using attention mechanisms to enhance segmentation accuracy.\"},{\"question\":\"How was the method validated and what were the main results?\",\"answer\":\"The method was evaluated on 2D MRI datasets from 304 patients (3773 images). It achieved Dice, MPA, MiouU, and FWIoU values of 0.987, 0.946, 0.897, and 0.899, outperforming existing methods.\"}]","Imaging segmentation mechanism for rectal tumors using improved U-Net | PDF",1789977248,25]