Computer-aided diagnostics in digital pathology: automated evaluation of early-phase pancreatic cancer in mice
Computer-aided diagnostics in digital pathology: automated evaluation of early-phase pancreatic cancer in mice
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DOI：
10.1007/s11548-014-1122-9
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发表时间：
2015-07-01
影响因子：
3
通讯作者：
Dekel, Shai
中科院分区：
文献类型：
--
作者：
Langer, Leeor;Binenbaum, Yoav;Dekel, Shai
数字病理诊断通常基于主观的定性测量。早期胰腺导管腺癌的小鼠模型提供了一个具有遗传突变和疾病阶段先验知识的受控环境。使用该模型可以将监督学习方法应用于数字病理学。一种用于胰腺腺癌组织学检测的计算机诊断系统被开发和测试。早期胰腺病变的病理h&e染色标本通过一个系统进行识别和评估，该系统使用自上而下的对象学习范式模拟癌症检测，模仿病理学家学习的方式。首先，识别并分割图像中的优势基元，即导管、细胞核和肿瘤基质。基于boost的机器学习技术用于管道分割、分类和异常值修剪。其次，一组传统上用于癌症诊断的形态学特征，提供定量的图像特征，用于量化细微的发现，如导管变形和核畸形。最后，训练一个视觉上可解释的预测模型，以区分正常组织和癌前病变，给出地面真值样本。使用十倍交叉验证的预测成功率为92%，独立测试集的预测成功率为93%。与最先进的分类算法进行比较，这些算法不能解释为可见特征，从而产生了单个原始特征对预测结果的贡献。定量图像分析和分类在早期胰腺腺癌的临床前组织学诊断中是成功的。使用带注释的轮廓加上可解释的监督学习方法和离群值修剪可以适应其他医学成像任务。使用可解释的监督学习技术可以提高CAD在组织病理学诊断中的成功率。
Digital pathology diagnostics are often based on subjective qualitative measures. A murine model of early-phase pancreatic ductal adenocarcinoma provides a controlled environment with a priori knowledge of the genetic mutation and stage of the disease. Use of this model enables the application of supervised learning methods to digital pathology. A computerized diagnostics system for histological detection of pancreatic adenocarcinoma was developed and tested.Pathological H&E-stained specimens with early pancreatic lesions were identified and evaluated with a system that models cancer detection using a top-down object learning paradigm, mimicking the way a pathologist learns. First, the dominant primitives were identified and segmented in the images, i.e., the ducts, nuclei and tumor stroma. A boost-based machine learning technique was used for duct segmentation, classification and outlier pruning. Second, a set of morphological features traditionally used for cancer diagnosis which provides quantitative image features was employed to quantify subtle findings such as duct deformation and nuclei malformations. Finally, a visually interpretable predictive model was trained to distinguish between normal tissue and premalignant cancer lesions, given ground truth samples.A predictive success rate of 92 % was achieved using tenfold cross-validation and 93 % on an independent test set. Comparison was made with state-of-the-art classification algorithms that are not interpretable as visible features yielded the contribution of individual primitive features to the prediction outcome.Quantitative image analysis and classification were successful in preclinical histology diagnosis for early-stage pancreatic adenocarcinoma. The usage of annotated contours coupled with interpretable supervised learning methods and outlier pruning can be adapted to other medical imaging tasks. The usage of interpretable supervised learning techniques may improve the success of CAD in histopathological diagnosis.