The high cure rate of cancer is inextricably linked to physicians' accuracy in diagnosis and treatment, therefore a model that can accomplish high-precision tumor segmentation has become a necessity in many applications of the medical industry. It can effectively lower the rate of misdiagnosis while considerably lessening the burden on clinicians. However, fully automated target organ segmentation is problematic due to the irregular stereo structure of 3D volume organs. As a basic model for this class of real applications, U-Net excels. It can learn certain global and local features, but still lacks the capacity to grasp spatial long-range relationships and contextual information at multiple scales. This paper proposes a tumor segmentation model MPU-Net for patient volume CT images, which is inspired by Transformer with a global attention mechanism. By combining image serialization with the Position Attention Module, the model attempts to comprehend deeper contextual dependencies and accomplish precise positioning. Each layer of the decoder is also equipped with a multi-scale module and a cross-attention mechanism. The capability of feature extraction and integration at different levels has been enhanced, and the hybrid loss function developed in this study can better exploit high-resolution characteristic information. Moreover, the suggested architecture is tested and evaluated on the Liver Tumor Segmentation Challenge 2017 (LiTS 2017) dataset. Compared with the benchmark model U-Net, MPU-Net shows excellent segmentation results. The dice, accuracy, precision, specificity, IOU, and MCC metrics for the best model segmentation results are 92.17%, 99.08%, 91.91%, 99.52%, 85.91%, and 91.74%, respectively. Outstanding indicators in various aspects illustrate the exceptional performance of this framework in automatic medical image segmentation.

翻译：癌症的高治愈率与医生诊断和治疗的精确性密不可分，因此能够实现高精度肿瘤分割的模型已成为医疗行业众多应用中的必要工具。该模型可有效降低误诊率，同时显著减轻临床医生的工作负担。然而，由于三维容积器官的不规则立体结构，全自动目标器官分割仍存在困难。作为此类实际应用的基础模型，U-Net表现优异，能够学习一定的全局与局部特征，但仍缺乏捕捉空间长距离关系及多尺度上下文信息的能力。本文提出了一种针对患者容积CT图像的肿瘤分割模型MPU-Net，该模型受具有全局注意力机制的Transformer启发。通过图像序列化与位置注意力模块（Position Attention Module）的结合，模型试图理解更深层的上下文依赖关系并实现精确定位。解码器的每一层还配备了多尺度模块与交叉注意力机制，增强了不同层次的特征提取与整合能力。本研究开发的混合损失函数能更好地利用高分辨率特征信息。此外，所提出的架构在2017年肝脏肿瘤分割挑战赛（LiTS 2017）数据集上进行了测试与评估。与基准模型U-Net相比，MPU-Net展现了出色的分割结果。最佳模型分割结果的Dice、准确率、精确率、特异性、IOU和MCC指标分别为92.17%、99.08%、91.91%、99.52%、85.91%和91.74%。各项指标的优异表现证明了该框架在自动医学图像分割中的出色性能。