Department of Mathematics, National University of Defense Technology, Changsha, China.
Academy of Military Sciences of the People's Liberation Army, Beijing, China.
Comput Methods Programs Biomed. 2024 Jun;250:108178. doi: 10.1016/j.cmpb.2024.108178. Epub 2024 Apr 21.
Gland segmentation of pathological images is an essential but challenging step for adenocarcinoma diagnosis. Although deep learning methods have recently made tremendous progress in gland segmentation, they have not given satisfactory boundary and region segmentation results of adjacent glands. These glands usually have a large difference in glandular appearance, and the statistical distribution between the training and test sets in deep learning is inconsistent. These problems make networks not generalize well in the test dataset, bringing difficulties to gland segmentation and early cancer diagnosis.
To address these problems, we propose a Variational Energy Network named VENet with a traditional variational energy L loss for gland segmentation of pathological images and early gastric cancer detection in whole slide images (WSIs). It effectively integrates the variational mathematical model and the data-adaptability of deep learning methods to balance boundary and region segmentation. Furthermore, it can effectively segment and classify glands in large-size WSIs with reliable nucleus width and nucleus-to-cytoplasm ratio features.
The VENet was evaluated on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset, the Colorectal Adenocarcinoma Glands (CRAG) dataset, and the self-collected Nanfang Hospital dataset. Compared with state-of-the-art methods, our method achieved excellent performance for GlaS Test A (object dice 0.9562, object F1 0.9271, object Hausdorff distance 73.13), GlaS Test B (object dice 94.95, object F1 95.60, object Hausdorff distance 59.63), and CRAG (object dice 95.08, object F1 92.94, object Hausdorff distance 28.01). For the Nanfang Hospital dataset, our method achieved a kappa of 0.78, an accuracy of 0.9, a sensitivity of 0.98, and a specificity of 0.80 on the classification task of test 69 WSIs.
The experimental results show that the proposed model accurately predicts boundaries and outperforms state-of-the-art methods. It can be applied to the early diagnosis of gastric cancer by detecting regions of high-grade gastric intraepithelial neoplasia in WSI, which can assist pathologists in analyzing large WSI and making accurate diagnostic decisions.
病理性图像的腺体分割是腺癌诊断的一个重要但具有挑战性的步骤。尽管深度学习方法最近在腺体分割方面取得了巨大进展，但它们并没有给出相邻腺体的边界和区域分割的令人满意的结果。这些腺体通常在腺体外观上有很大的差异，并且深度学习中的训练集和测试集之间的统计分布不一致。这些问题使得网络在测试数据集上不能很好地泛化，给腺体分割和早期癌症诊断带来困难。
为了解决这些问题，我们提出了一种名为 VENet 的变分能量网络，它使用传统的变分能量 L 损失来进行病理性图像的腺体分割和全切片图像（WSI）中的早期胃癌检测。它有效地整合了变分数学模型和深度学习方法的数据适应性，以平衡边界和区域分割。此外，它可以有效地分割和分类大尺寸 WSI 中的腺体，具有可靠的核宽度和核质比特征。
VENet 在 2015 年 MICCAI 腺体分割挑战赛（GlaS）数据集、结直肠腺癌腺体（CRAG）数据集和我们自己收集的南方医院数据集上进行了评估。与最先进的方法相比，我们的方法在 GlaS Test A（目标骰子 0.9562，目标 F1 0.9271，目标 Hausdorff 距离 73.13）、GlaS Test B（目标骰子 94.95，目标 F1 95.60，目标 Hausdorff 距离 59.63）和 CRAG（目标骰子 95.08，目标 F1 92.94，目标 Hausdorff 距离 28.01）上取得了优异的性能。对于南方医院数据集，我们的方法在 69 张测试 WSI 的分类任务上实现了kappa 值为 0.78、准确率为 0.9、敏感度为 0.98 和特异性为 0.80。
实验结果表明，所提出的模型能够准确地预测边界，并且优于最先进的方法。它可以通过检测 WSI 中高级胃上皮内瘤变的区域，应用于胃癌的早期诊断，帮助病理学家分析大尺寸 WSI 并做出准确的诊断决策。
