本文介绍了一种临床适用的计算机分割模型，旨在通过捕捉医学图像中的每个小的和复杂的变化，整合第二意见，减少人为错误，提供临床腺体信息细节。该模型包括增强学习能力，提取密集的多尺度腺体特异性特征，恢复串联过程中的语义间隙，有效处理分辨率降级和梯度消失问题。该模型具有三个建议的模块，分别是编码器和解码器中的Atrous卷积残差学习模块，跳过连接路径中的残差注意模块，以及作为过渡和输出层的Atrous卷积过渡模块。此外，还采用像块采样、染色归一化、数据增强等预处理技术来提高其泛化能力。为了验证其强健性和增强网络的不变性以应对数字变异，我们进行了详细的实验，采用三个不同的公共数据集，即GlaS（腺体分割挑战赛）、CRAG（结直肠腺癌腺体）和LC-25000（肺结肠-25000）数据集，以及一个私人HosC（医院结肠）数据集。 该模型实现了具有竞争性的腺体检测结果，F1得分（GlaS（测试A（0.957），测试B（0.926）），CRAG（0.935），LC 25000（0.922），HosC（0.963））；和腺体分割结果具有目标Dice指数（GlaS（测试A（0.961），测试B（0.933）），CRAG（0.961），LC-25000（0.940），HosC（0.929））和目标Hausdorff距离（GlaS（测试A（21.77）和测试B（69.74）），CRAG（87.63），LC-25000（95.85），HosC（83.29））。此外，对于最终分割结果，还结合了由熟练的病理学家提供的验证分数（GlaS（测试A（0.945），测试B（0.937）），CRAG（0.934），LC-25000（0.911），HosC（0.928）），以证明其在临床应用的可行性和适当性。 该系统将协助病理学家提供参考视角，在结肠组织学形态学评估过程中提供准确的诊断。版权所有 © 2023 Elsevier Ltd.

A clinically compatible computerized segmentation model is presented here that aspires to supply clinical gland informative details by seizing every small and intricate variation in medical images, integrate second opinions, and reduce human errors.It comprises of enhanced learning capability that extracts denser multi-scale gland-specific features, recover semantic gap during concatenation, and effectively handle resolution-degradation and vanishing gradient problems. It is having three proposed modules namely Atrous Convolved Residual Learning Module in the encoder as well as decoder, Residual Attention Module in the skip connection paths, and Atrous Convolved Transitional Module as the transitional and output layer. Also, pre-processing techniques like patch-sampling, stain-normalization, augmentation, etc. are employed to develop its generalization capability. To verify its robustness and invigorate network invariance against digital variability, extensive experiments are carried out employing three different public datasets i.e., GlaS (Gland Segmentation Challenge), CRAG (Colorectal Adenocarcinoma Gland) and LC-25000 (Lung Colon-25000) dataset and a private HosC (Hospital Colon) dataset.The presented model accomplished combative gland detection outcomes having F1-score (GlaS(Test A(0.957), Test B(0.926)), CRAG(0.935), LC 25000(0.922), HosC(0.963)); and gland segmentation results having Object-Dice Index (GlaS(Test A(0.961), Test B(0.933)), CRAG(0.961), LC-25000(0.940), HosC(0.929)), and Object-Hausdorff Distance (GlaS(Test A(21.77) and Test B(69.74)), CRAG(87.63), LC-25000(95.85), HosC(83.29)). In addition, validation score (GlaS (Test A(0.945), Test B(0.937)), CRAG(0.934), LC-25000(0.911), HosC(0.928)) supplied by the proficient pathologists is integrated for the end segmentation results to corroborate the applicability and appropriateness for assistance at the clinical level applications.The proposed system will assist pathologists in devising precise diagnoses by offering a referential perspective during morphology assessment of colon histopathology images.Copyright © 2023 Elsevier Ltd. All rights reserved.