直肠癌在磁共振成像(MR)图像中的分割可以帮助医生确定直肠癌的程度和严重程度，因此直肠肿瘤的分割对于改善直肠癌诊断的准确性至关重要。然而，由于直肠肿瘤的形状存在显著的变异性，并且肿瘤和周围组织难以区分，因此准确地分割直肠癌的区域仍然是一项具有挑战性的任务。此外，在直肠肿瘤分割的早期研究中，大多数深度学习方法都基于卷积神经网络(CNN)，传统的CNN具有较小的感受野，只能捕获局部信息而忽略图像的全局信息。然而，全局信息在直肠肿瘤分割中发挥着至关重要的作用，因此传统的基于CNN的方法通常无法实现令人满意的分割结果。在本文中，我们提出了一个编码器-解码器网络，名为“双并行网络(DuPNet)”，它融合了转换器和传统的CNN，以捕获全局和局部信息。同时，为了在编码器和解码器之间的跳过连接中捕获不同尺度的特征，并避免精度损失和参数减少，我们设计了一个特征自适应块(FAB)。此外，为了有效利用直肠肿瘤形状的先验信息，我们设计了一个高斯混合先验，并将其嵌入到转换器的自注意机制中，从而实现了稳健的特征表示和准确的分割结果。我们进行了广泛的消融实验证明了我们提出的双并行编码器、FAB和高斯混合先验在山西省癌症医院数据集上的有效性。在与最先进的方法的实验比较中，我们的方法在测试集上实现了89.34%的平均交叉点联合(MIoU)。此外，我们评估了我们的方法在新华医院数据集上的泛化能力，有希望的结果验证了我们方法的优越性。版权所有 © 2023 Elsevier Inc.。保留所有权利。

Segmentation of rectal cancerous regions from Magnetic Resonance (MR) images can help doctor define the extent of the rectal cancer and judge the severity of rectal cancer, so rectal tumor segmentation is crucial to improve the accuracy of rectal cancer diagnosis. However, accurate segmentation of rectal cancerous regions remains a challenging task due to the shape of rectal tumor has significant variations and the tumor and surrounding tissue are indistinguishable. In addition, in the early research on rectal tumor segmentation, most deep learning methods were based on convolutional neural networks (CNNs), and traditional CNN have small receptive field, which can only capture local information while ignoring the global information of the image. Nevertheless, the global information plays a crucial role in rectal tumor segmentation, so traditional CNN-based methods usually cannot achieve satisfactory segmentation results. In this paper, we propose an encoder-decoder network named Dual Parallel Net (DuPNet), which fuses transformer and classical CNN for capturing both global and local information. Meanwhile, as for capture features at different scales as well as to avoid accuracy loss and parameters reduction, we design a feature adaptive block (FAB) in skip connection between encoder and decoder. Furthermore, in order to utilize the apriori information of rectal tumor shape effectively, we design a Gaussian Mixture prior and embed it in self-attention mechanism of the transformer, leading to robust feature representation and accurate segmentation results. We have performed extensive ablation experiments to verify the effectiveness of our proposed dual parallel encoder, FAB and Gaussian Mixture prior on the dataset from the Shanxi Cancer Hospital. In the experimental comparison with the state-of-the-art methods, our method achieved a Mean Intersection over Union (MIoU) of 89.34% on the test set. In addition to that, we evaluated the generalizability of our method on the dataset from Xinhua Hospital, the promising results verify the superiority of our method.Copyright © 2023 Elsevier Inc. All rights reserved.