A neural network for glomerulus classification based on histological images of kidney biopsy

BMC Medical Informatics and Decision Making

Antonio Brunetti¹  Domenico Buongiorno¹  Ruggero Lemma¹  Francesco Saverio Debitonto¹  Andrea Guerriero¹  Giacomo Donato Cascarano¹  Irio De Feudis¹  Vitoantonio Bevilacqua¹  Francesco Pesce²  Umberto Venere²  Loreto Gesualdo²  Maria Teresa Rocchetti²  Silvia Matino²  Michele Rossini² 

[1] Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari;Department of Emergency and Organ Transplantation, Nephrology Unit University of Bari Aldo Moro;

关键词: CKD;    Kidney;    Glomerulus classification;    Morphological features;    Texture features;    ANN;   

DOI  :  10.1186/s12911-021-01650-3

来源: DOAJ

【 摘 要 】

Abstract Background 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. Results 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). Conclusions 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.

【 授权许可】

Unknown