Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study

Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics. 

/pdf/next-generation-morphometry-for-pathomics-data-mining-in-kz4vs1l1.pdf

Next-Generation Morphometry for pathomics-data mining in histopathology

To improve the diagnosis of Lupus Nephritis (LN), a multilevel LN image segmentation method is developed in this paper based on an improved slime mould algorithm. The search of the optimal threshold set is key to multilevel thresholding image segmentation (MLTIS). It is well known that swarm-based methods are more efficient than the traditional methods because of the high complexity in finding the optimal threshold, especially when performing image partitioning at high threshold levels. However, swarm-based methods tend to obtain the poor quality of the found segmentation thresholds and fall into local optima during the process of segmentation. Therefore, this paper proposes an ASMA-based MLTIS approach by combining an improved slime mould algorithm (ASMA), where ASMA is mainly implemented by introducing the position update mechanism of the artificial bee colony (ABC) into the SMA. To prove the superiority of the ASMA-based MLTIS method, we first conducted a comparison experiment between ASMA and 11 peers using 30 test functions. The experimental results fully demonstrate that ASMA can obtain high-quality solutions and almost does not suffer from premature convergence. Moreover, using standard images and LN images, we compared the ASMA-based MLTIS method with other peers and evaluated the segmentation results using three evaluation indicators called PSNR, SSIM, and FSIM. The proposed ASMA can be an excellent swarm intelligence optimization method that can maintain a delicate balance during the segmentation process of LN images, and thus the ASMA-based MLTIS method has great potential to be used as an image segmentation method for LN images. The lastest updates for the SMA algorithm are available in https://aliasgharheidari.com/SMA.html. 

An efficient multilevel thresholding image segmentation method based on the slime mould algorithm with bee foraging mechanism: A real case with lupus nephritis images

Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies

Abstract Background Transplant nephropathology is a highly specialized field of pathology comprising both the evaluation of organ donor biopsy for organ allocation and post-transplant graft biopsy for assessment of rejection or graft damage. The introduction of digital pathology with whole-slide imaging (WSI) in clinical research, trials and practice has catalyzed the application of artificial intelligence (AI) for histopathology, with development of novel machine-learning models for tissue interrogation and discovery. We aimed to review the literature for studies specifically applying AI algorithms to WSI-digitized pre-implantation kidney biopsy. Methods A systematic search was carried out in the electronic databases PubMed-MEDLINE and Embase until 25th September, 2021 with a combination of the key terms “kidney”, “biopsy”, “transplantation” and “artificial intelligence” and their aliases. Studies dealing with the application of AI algorithms coupled with WSI in pre-implantation kidney biopsies were included. The main theme addressed was detection and quantification of tissue components. Extracted data were: author, year and country of the study, type of biopsy features investigated, number of cases, type of algorithm deployed, main results of the study in terms of diagnostic outcome, and the main limitations of the study. Results Of 5761 retrieved articles, 7 met our inclusion criteria. All studies focused largely on AI-based detection and classification of glomerular structures and to a lesser extent on tubular and vascular structures. Performance of AI algorithms was excellent and promising. Conclusion All studies highlighted the importance of expert pathologist annotation to reliably train models and the need to acknowledge clinical nuances of the pre-implantation setting. Close cooperation between computer scientists and practicing as well as expert renal pathologists is needed, helping to refine the performance of AI-based models for routine pre-implantation kidney biopsy clinical practice. 

/pdf/artificial-intelligence-applications-for-pre-implantation-21kcgrq1.pdf

Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review

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

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