Cascarano Giacomo Donato, Debitonto Francesco Saverio, Lemma Ruggero, Brunetti Antonio, Buongiorno Domenico, De Feudis Irio, Guerriero Andrea, Venere Umberto, Matino Silvia, Rocchetti Maria Teresa, Rossini Michele, Pesce Francesco, Gesualdo Loreto, Bevilacqua Vitoantonio
Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy.
Apulian Bioengineering s.r.l., Modugno, BA, Italy.
BMC Med Inform Decis Mak. 2021 Nov 1;21(Suppl 1):300. doi: 10.1186/s12911-021-01650-3.
Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results. CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists.
We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes. We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation < 0.00), respectively. We also obtained good precision (0.9844 ± 0.0111) and recall (0.9310 ± 0.0153).
Results on the test set confirm that the proposed workflow is consistent and reliable for the investigated domain, and it can support the clinical practice of discriminating the two classes of glomeruli. Analyses on misclassifications show that the involved images are usually affected by staining artefacts or present partial sections due to slice preparation and staining processes. In clinical practice, however, pathologists discard images showing such artefacts.
基于医学图像的计算机辅助诊断（CAD）系统可在决策过程中为医生提供支持。在过去几十年中，研究人员已在多个医学领域提出了CAD系统，并取得了有前景的成果。CAD系统在数字病理学中发挥着重要作用，通过标准化和客观的工作流程支持病理学家分析活检切片。在本研究中，我们设计并测试了一种基于图像处理技术和机器学习的新型CAD系统模块，其目的是对影响肾小体（肾小球）的硬化和非硬化状况进行分类。这种区分对病理学家进行活检切片评估很有用。
我们收集了19名捐赠者肾脏的26张经高碘酸-希夫染色的数字切片。专业病理学家进行了切片制备、数字采集和肾小球标注。在设置分类器之前，我们从标注区域评估了几种特征提取技术。然后，经过特征约简过程，再使用浅层人工神经网络对肾小球类别进行区分。我们使用一个独立数据集（即处理训练过程中未使用的图像）评估了该工作流程。训练算法进行十次独立运行和评估后，马修斯相关系数（MCC）和准确率分别达到0.95（±0.01）和0.99（标准差<0.00）。我们还获得了良好的精确率（0.9844±0.0111）和召回率（0.9310±0.0153）。
测试集结果证实，所提出的工作流程对于所研究的领域是一致且可靠的，并且可以支持区分两类肾小球的临床实践。对错误分类的分析表明，所涉及的图像通常受到染色伪影的影响，或者由于切片制备和染色过程而呈现部分切片。然而，在临床实践中，病理学家会丢弃显示此类伪影的图像。
