An Innovative Neural Network Framework for Glomerulus Classification Based on Morphological and Texture Features Evaluated in Histological Images of Kidney Biopsy

Cascarano, Giacomo Donato; Debitonto, Francesco Saverio; Lemma, Ruggero; Brunetti, Antonio; Buongiorno, Domenico; Feudis, Irio De; Guerriero, Andrea; Rossini, Michele; Pesce, Francesco; Gesualdo, Loreto; Bevilacqua, Vitoantonio

doi:10.1007/978-3-030-26766-7_66

Cited by 10 publications

(17 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the realm of digital pathology, several recent studies have proposed CAD systems for glomerulus identification and classification in renal biopsies [1][2][3][4][5][6][7][8]. The eligibility for transplantation of a kidney retrieved from Expanded Criteria Donors (ECD) relies on rush histological examination of the organ to evaluate suitability for transplant [9].…”

Section: Introductionmentioning

confidence: 99%

“…In our previous works we focused on other kidney biopsies analysis tasks, such as classification of tubules and vessels [15] and classification of non-sclerotic and sclerotic glomeruli [5]. In this work, we propose a CAD system to address the segmentation and the classification tasks of glomeruli, in order to obtain a reliable estimate of Karpinski histological score.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections

et al. 2020

Self Cite

View full text Add to dashboard Cite

The evaluation of kidney biopsies performed by expert pathologists is a crucial process for assessing if a kidney is eligible for transplantation. In this evaluation process, an important step consists of the quantification of global glomerulosclerosis, which is the ratio between sclerotic glomeruli and the overall number of glomeruli. Since there is a shortage of organs available for transplantation, a quick and accurate assessment of global glomerulosclerosis is essential for retaining the largest number of eligible kidneys. In the present paper, the authors introduce a Computer-Aided Diagnosis (CAD) system to assess global glomerulosclerosis. The proposed tool is based on Convolutional Neural Networks (CNNs). In particular, the authors considered approaches based on Semantic Segmentation networks, such as SegNet and DeepLab v3+. The dataset has been provided by the Department of Emergency and Organ Transplantations (DETO) of Bari University Hospital, and it is composed of 26 kidney biopsies coming from 19 donors. The dataset contains 2344 non-sclerotic glomeruli and 428 sclerotic glomeruli. The proposed model consents to achieve promising results in the task of automatically detecting and classifying glomeruli, thus easing the burden of pathologists. We get high performance both at pixel-level, achieving mean F-score higher than 0.81, and Weighted Intersection over Union (IoU) higher than 0.97 for both SegNet and Deeplab v3+ approaches, and at object detection level, achieving 0.924 as best F-score for non-sclerotic glomeruli and 0.730 as best F-score for sclerotic glomeruli.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several reports have been published describing machine learning algorithms in mouse and human renal tissue. [2][3][4] Taken together, these approaches demonstrate that automated tools are feasible for renal pathology classification. For this proof of principle study, three glomerular disorders are used: minimal change disease (MCD), membranous nephropathy (MN), and thin-basement membrane nephropathy (TBMN).…”

Section: Introductionmentioning

confidence: 79%

“…Color normalization was not used for segmentation as there was a decrease in performance. Three structures were segmented: (1) luminal (space inside the Bowman's capsule and the capillary lumen), (2) glomerular tuft (the glomerular basement membrane (GBM) and mesangial matrix), and (3) nuclei. For each structure, the ROIs were transformed into a color representation that was optimal for the given structure, followed by Otsu's binary thresholding, and a 3x3 median filter to remove noise in the estimated segmented structures.…”

Section: Preprocessingmentioning

confidence: 99%

Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification

2022

View full text Add to dashboard Cite

Background Pathologists use multiple microscopy modalities to assess renal biopsies. Besides usual diagnostic features, some changes are too subtle to be properly defined. Computational approaches have the potential to systematically quantitate subvisual clues, provide pathogenetic insight, and link to clinical outcomes. To this end, a proof of principle study is presented demonstrating that explainable biomarkers through machine learning can distinguish between glomerular disorders at the light microscopy level. Methods The proposed system employed image analysis techniques and extracted 233 explainable biomarkers related to color, morphology, and microstructural texture. Traditional machine learning was then used to classify minimal change disease (MCD), membranous nephropathy (MN), and thin-basement membrane nephropathy (TBMN) diseases on a glomerular and patient-level basis. Results The final model combined the Gini feature importance set and Linear Discriminant Analysis classifier. Six morphological (nuclei-to-glomerular tuft area, nuclei-to-glomerular area, glomerular tuft thickness > 10, glomerular tuft thickness > 3, total glomerular tuft thickness, and glomerular circularity) and four microstructural texture features (luminal contrast using wavelets, nuclei energy using wavelets, nuclei variance using color vector LBP, and glomerular correlation using GLCM) were together the best performing biomarkers. Accuracies of 76.86% and 86.67% were obtained for glomerular and patient-level classification, respectively. Conclusion Computational methods using explainable glomerular biomarkers have diagnostic value and are compatible with our existing knowledge of disease pathogenesis. Furthermore, this algorithm can be applied to clinical datasets for novel prognostic and mechanistic biomarker discovery.

show abstract

“…Such fine-grained characterization is challenging as the available data is rare and their distribution is highly imbalanced. For instance, the presence of obsolescent glomerulosclerosis is naturally much higher than solidified or disappearing glomerulosclerosis [42], [43], leading to the technical difficulty that is well known as the imbalanced classification problem [44]- [46]. 1) Self-supervised learning method: Self-supervised learning represents a family of learning algorithms that could learn the hidden regularities from using data without manual annotations (e.g., unannotated renal pathological images) [47].…”

Section: A Glomerulosclerosis Classificationmentioning

confidence: 99%

Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining

Yao¹,

Lu²,

Long³

et al. 2022

Preprint

View full text Add to dashboard Cite

The quantitative detection, segmentation, and characterization of glomeruli from high-resolution whole slide imaging (WSI) play essential roles in the computer-assisted diagnosis and scientific research in digital renal pathology. Historically, such comprehensive quantification requires extensive programming skills in order to be able to handle heterogeneous and customized computational tools. To bridge the gap of performing glomerular quantification for non-technical users, we develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a single line of command. Additionally, we release a large-scale collection of 30,000 unlabeled glomerular images to further facilitate the algorithmic development of self-supervised deep learning. The inputs of the Glo-In-One toolkit are WSIs, while the outputs are (1) WSIlevel multi-class circle glomerular detection results (which can be directly manipulated with ImageScope), (2) glomerular image patches with segmentation masks, and (3) different lesion types. In the current version, the fine-grained global glomerulosclerosis (GGS) characterization is provided, including assessedsolidified (S-GGS, associated with hypertension-related injury), disappearing (D-GGS, a further end result of the SGGS becoming contiguous with fibrotic interstitium), and obsolescent (O-GGS, nonspecific GGS increasing with aging) glomeruli. To leverage the performance of the Glo-In-One toolkit, we introduce selfsupervised deep learning to glomerular quantification via largescale web image mining. The GGS fine-grained classification model achieved a decent performance compared with baseline supervised methods while only using 10% of the annotated data. The glomerular detection achieved an average precision of 0.627 with circle representations, while the glomerular segmentation achieved a 0.955 patch-wise Dice Similarity Coefficient (DSC). In this paper, we develop and release an open-source Glo-In-One toolkit, a software with holistic glomerular detection, segmentation, and lesion characterization. This toolkit is userfriendly to non-technical users via a single line of command.The toolbox and the 30,000 web mined glomerular images have been made publicly available at https://github.com/hrlblab/ Glo-In-One.

show abstract

An Innovative Neural Network Framework for Glomerulus Classification Based on Morphological and Texture Features Evaluated in Histological Images of Kidney Biopsy

Cited by 10 publications

References 20 publications

Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections

Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections

Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification

Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining

Contact Info

Product

Resources

About