Department of Computer Science, University of Saskatchewan, Canada.
Department of Neurology and Neurological, University of Stanford, United States.
Int J Med Inform. 2022 Mar;159:104669. doi: 10.1016/j.ijmedinf.2021.104669. Epub 2021 Dec 31.
Colorectal cancer is one of the leading causes of cancer-related death, worldwide. Early detection of suspicious tissues can significantly improve the survival rate. In this study, the performance of a wide variety of deep learning-based architectures is evaluated for automatic tumor segmentation of colorectal tissue samples. The proposed approach highlights the utility of incorporating convolutional neural network modules and transfer learning in the encoder part of a segmentation architecture for histopathology image analysis. A comparative and extensive experiment was conducted on a challenging histopathological segmentation task to demonstrate the effectiveness of incorporating deep modules in the segmentation encoder-decoder network as well as the contributions of its components. Experimental results demonstrate that shared DenseNet and LinkNet architecture is promising, achieves the state-of-the-art performance, and outperforms other methods with a dice similarity index of 82.74%±1.77, accuracy of 87.07%±1.56, and f1-score value of 82.79%±1.79.
结直肠癌是全球癌症相关死亡的主要原因之一。早期检测可疑组织可以显著提高生存率。在这项研究中，评估了各种基于深度学习的架构在自动分割结直肠组织样本中的性能。所提出的方法强调了在分割架构的编码器部分中结合卷积神经网络模块和迁移学习的效用，用于组织病理学图像分析。在具有挑战性的组织病理学分割任务上进行了比较和广泛的实验，以证明在分割编码器-解码器网络中结合深度模块的有效性，以及其组件的贡献。实验结果表明，共享的 DenseNet 和 LinkNet 架构很有前景，达到了最先进的性能，并以 82.74%±1.77 的迪塞相似系数、87.07%±1.56 的准确率和 82.79%±1.79 的 f1 分数值优于其他方法。
