胃肠道（GI）癌症是一种影响消化器官的恶性肿瘤。在放射治疗期间，放射肿瘤学家必须准确地将X射线束瞄准肿瘤，同时避免影响胃和肠道的未受影响区域。因此，精确的自动化GI图像分割在临床实践中迫切需要。虽然全卷积网络（FCN）和U-Net框架在医学图像分割方面表现出色，但它们对模拟器的长程依赖性建模能力受到了卷积核受限接受域的限制。变形器由于其固有的全局自注意机制具有强大的全局建模能力。TransUnet模型通过混合CNN-transformer编码器发挥了卷积神经网络（CNN）和变形器模型的优势。然而，编码器中的高低级特征串联在融合全局和局部信息方面效果不佳。为了克服这个限制，我们提出了一种创新的基于变形器的医学图像分割架构，称为BiFTransNet，该架构在解码器阶段引入了一个BiFusion模块，通过从各种模块中集成特征，实现了有效的全局和局部特征融合。此外，还引入了多级损失（ML）策略，以监督每个解码器层的学习过程，并优化在不同尺度上使用全球和局部融合的上下文特征。我们的方法在UW-Madison胃肠道分割数据集上获得了89.51%的Dice分数和86.54%的交并比（IoU）分数。此外，我们的方法在Synapse多器官分割数据集上达到了78.77%的Dice分数和27.94%的Hausdorff距离（HD）。与现有技术方法相比，我们提出的方法在胃肠道分割任务中实现了更优越的分割性能。更重要的是，我们的方法可以轻松扩展到CT和MRI等不同模态的医学分割。我们的方法实现了临床多模式医学分割，并为临床放射治疗计划提供决策支持。版权©2023 Elsevier有限公司。保留所有权利。

Gastrointestinal (GI) cancer is a malignancy affecting the digestive organs. During radiation therapy, the radiation oncologist must precisely aim the X-ray beam at the tumor while avoiding unaffected areas of the stomach and intestines. Consequently, accurate, automated GI image segmentation is urgently needed in clinical practice. While the fully convolutional network (FCN) and U-Net framework have shown impressive results in medical image segmentation, their ability to model long-range dependencies is constrained by the convolutional kernel's restricted receptive field. The transformer has a robust capacity for global modeling owing to its inherent global self-attention mechanism. The TransUnet model leverages the strengths of both the convolutional neural network (CNN) and transformer models through a hybrid CNN-transformer encoder. However, the concatenation of high- and low-level features in the decoder is ineffective in fusing global and local information. To overcome this limitation, we propose an innovative transformer-based medical image segmentation architecture called BiFTransNet, which introduces a BiFusion module into the decoder stage, enabling effective global and local feature fusion by enabling feature integration from various modules. Further, a multilevel loss (ML) strategy is introduced to oversee the learning process of each decoder layer and optimize the use of globally and locally fused contextual features at different scales. Our method achieved a Dice score of 89.51% and an intersection-over-union (IoU) score of 86.54% on the UW-Madison Gastrointestinal Segmentation dataset. Moreover, our method attained a Dice score of 78.77% and a Hausdorff distance (HD) of 27.94% on the Synapse Multi-organ Segmentation dataset. Compared with the state-of-the-art methods, our proposed method achieves superior segmentation performance in gastrointestinal segmentation tasks. More significantly, our method can be easily extended to medical segmentation in different modalities such as CT and MRI. Our method achieves clinical multimodal medical segmentation and provides decision supports for clinical radiotherapy plans.Copyright © 2023 Elsevier Ltd. All rights reserved.