Title: Gland Segmentation in Histopathological Images by Deep Neural Network Show Chinese title

Title: 基于深度神经网络的组织病理学图像腺体分割

Abstract: Histology method is vital in the diagnosis and prognosis of cancers and many other diseases. For the analysis of histopathological images, we need to detect and segment all gland structures. These images are very challenging, and the task of segmentation is even challenging for specialists. Segmentation of glands determines the grade of cancer such as colon, breast, and prostate. Given that deep neural networks have achieved high performance in medical images, we propose a method based on the LinkNet network for gland segmentation. We found the effects of using different loss functions. By using Warwick-Qu dataset, which contains two test sets and one train set, we show that our approach is comparable to state-of-the-art methods. Finally, it is shown that enhancing the gland edges and the use of hematoxylin components can improve the performance of the proposed model. Show Chinese abstract

Abstract: 组织学方法在癌症和其他许多疾病的诊断和预后中至关重要。 对于组织病理图像的分析，我们需要检测和分割所有腺体结构。 这些图像非常具有挑战性，分割任务对专家来说也极具挑战性。 腺体的分割决定了癌症的等级，如结肠、乳腺和前列腺。 鉴于深度神经网络在医学图像中取得了高性能，我们提出了一种基于LinkNet网络的腺体分割方法。 我们发现了使用不同损失函数的效果。 通过使用Warwick-Qu数据集，该数据集包含两个测试集和一个训练集，我们展示了我们的方法与最先进的方法相当。 最后，结果显示增强腺体边缘和使用苏木精成分可以提高所提出模型的性能。