Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains

Jayapandian, Catherine P.; Chen, Yijiang; Janowczyk, Andrew; Palmer, Matthew; Cassol, Clarissa; Sekulic, Miroslav; Hodgin, Jeffrey B.; Zee, Jarcy; Hewitt, Stephen M.; O’Toole, John F.; Toro, Paula; Sedor, John R.; Barisoni, Laura; Madabhushi, Anant; Dell, Katherine MacRae; Schachere, M.; Negrey, Jacob D.; Lemley, Kevin V.; Lim, Ee Peng; Srivastava, Tarak; Garrett, Alfred J.; Sethna, Christine B.; Laurent, Karine; Appel, Gerald B.; Toledo, Marcelo; Greenbaum, Laurence; Wang, C.; Kang, Chengdong; Adler, Sharon G.; Nast, Cynthia C.; LaPage, Janine; Stroger, John H.; Athavale, Ambarish M.; Itteera, M.; Neu, Alicia M.; Boynton, Sara A.; Fervenza, Fernando C.; Hogan, Marie C.; Lieske, John C.; Chernitskiy, Vladimir; Kaskel, Frederick J.; Kumar, Navdeep; Flynn, P.; Kopp, Jeffrey B.; Blake, J. Thomas; Trachtman, Howard; Zhdanova, Olga; Modersitzki, Frank; Vento, Suzanne; Lafayette, Richard A.; Mehta, Kshama; Gadegbeku, Crystal A.; Johnstone, Duncan B.; Quinn-Boyle, S.; Cattran, Dan; Hladunewich, Michelle A.; Reich, Heather N.; Ling, P.; Romano, M.; Fornoni, Alessia; Bidot, Carlos; Kretzler, Matthias; Gipson, Debbie S.; Williams, Amanda L.; Lavigne, Jeffrey R.; Derebail, Vimal K.; Gibson, Keisha; Froment, A.; Grubbs, S.; Holzman, Lawrence B.; Meyers, Kevin; Kallem, K.; Lalli, J.; Sambandam, Kamalanathan K.; Wang, Z.; Rogers, Marc J.; Jefferson, Ashley; Hingorani, Sangeeta; Tuttle, K.; Bray, Mark‐Anthony; Kelton, Molly L.; Cooper, Ann; Freedman, Benjamin; Lin, Jen Jar

doi:10.1016/j.kint.2020.07.044

Cited by 111 publications

(71 citation statements)

References 53 publications

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“…In general, deep learning performs better with increasing amounts of data [69], although this amount can vary substantially for different approaches. E.g., already a couple of annotated glomeruli can suffice to train a DL algorithm to detect them with high accuracy [70,71], while many thousands of annotations are required for the reliable detection of peritubular capillaries [72].…”

Section: Artificial Intelligence Machine and Deep Learningmentioning

confidence: 99%

“…Detection and segmentation of glomeruli in digital pictures of histological specimens or whole slide images (WSI) was one of the first and commonly used tasks, shown to be feasible in multiple stains [70,[93][94][95]. More recently, semantic multiclass segmentation of kidney histology was developed by several groups [72,96,97].…”

Section: Ai Applications For Nephropathologymentioning

confidence: 99%

“…The feasibility of multiclass segmentation in various stains commonly used in nephropathology diagnostics, i.e., Hematoxylin & Eosin (H&E), PAS, Jones-Silver, and trichrome using CNNs, was investigated in another study. These CNNs were developed on a large multicenter dataset of minimal change nephropathy biopsies, the NEPTUNE dataset [72,98]. Currently, this is the only study showing feasibility of kidney capillary segmentation, which required an enormous training effort.…”

Section: Ai Applications For Nephropathologymentioning

confidence: 99%

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How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

2021

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IgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney’s glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN’s pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex “big data,” requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.

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Section: Artificial Intelligence Machine and Deep Learningmentioning

confidence: 99%

Section: Ai Applications For Nephropathologymentioning

confidence: 99%

Section: Ai Applications For Nephropathologymentioning

confidence: 99%

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How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

2021

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“…We must however acknowledge that the nephropathologist’s clinical impression and diagnosis is based on contextual factors above and beyond visual inspection of a lesion in isolation.Nevertheless, by identifying WSI regions using CAMs that are highly indicative of a class label, our approach provides a quantitative basis by which to interpret the model-based predictions rather than viewing DL methods as black-box approaches. As such, our approach stands in contrast to other methods that rely on expert-driven annotations and segmentation algorithms that attempt to quantify histological regions and derive information for pathologic assessment (12, 15-18).…”

Section: Discussionmentioning

confidence: 99%

Deep learning driven quantification of interstitial fibrosis in kidney biopsies

Zheng

Cassol

Jung

et al. 2021

Preprint

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Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment by capturing the pathologic features. Using trichrome-stained whole slide images (WSIs) processed from human renal biopsies, we developed a deep learning (DL) framework that captured finer pathological structures at high resolution and overall context at the WSI-level to predict IFTA grade. WSIs (n=67) were obtained from The Ohio State University Wexner Medical Center (OSUWMC). Five nephropathologists independently reviewed them and provided fibrosis scores that were converted to IFTA grades: <=10% (None or minimal), 11-25% (Mild), 26-50% (Moderate), and >50% (Severe). The model was developed by associating the WSIs with the IFTA grade determined by majority voting (reference estimate). Model performance was evaluated on WSIs (n=28) obtained from the Kidney Precision Medicine Project (KPMP). There was good agreement on the IFTA grading between the pathologists and the reference estimate (Kappa=0.622±0.071). The accuracy of the DL model was 71.8±5.3% on OSUWMC and 65.0±4.2% on KPMP datasets, respectively. Identification of salient image regions by combining microscopic and WSI-level pathological features yielded visual representations that were consistent with the pathologist-based IFTA grading. Our approach to analyzing microscopic- and WSI-level changes in renal biopsies attempts to mimic the pathologist and provides a regional and contextual estimation of IFTA. Such methods can assist clinicopathologic diagnosis.Translational statementPathologists rely on interstitial fibrosis and tubular atrophy (IFTA) to indicate chronicity in kidney biopsies and provide a prognostic indicator of renal survival. Although guidelines for evaluation of IFTA exist, there is variability in IFTA estimation among pathologists. In this work, digitized kidney biopsies were independently reviewed by five nephropathologists and majority voting on their ratings was used to determine the IFTA grade. Using this information, a deep learning model was developed that captured microscopic and holistic features on the digitized biopsies and accurately predicted the IFTA grade. The study illustrates that deep learning can be utilized effectively to perform IFTA grading, thus enhancing conventional clinicopathologic diagnosis.

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“…They found that PAS-stained WSIs yielded the best concordance between pathologists and deep learning segmentation across all structures. 87 IF has also been specifically quantified with AIbased approaches. For example, Kolachalama et al at Boston University have established associations of AI detection of pathological fibrosis with renal survival by using the GoogLeNet Inception deep learning model, deployed with TENSORFLOW.…”

Section: H Y P O T H E S I S -D R I V E N / T a R G E T E D A L G O Rmentioning

confidence: 99%

Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples

Farris¹,

Vizcarra

Amgad

et al. 2021

Histopathology

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Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examplesWhole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.

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Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains

Cited by 111 publications

References 53 publications

How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

Deep learning driven quantification of interstitial fibrosis in kidney biopsies

Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples

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