Biology subjects

Besusparis, J.

Publications and source records attributed to Besusparis, J..

3 recordsLinked to original sources

bioRxiv2025

Morphometry-based detection of deep learning faults in glomerular segmentation

Deep learning-based segmentation has evolved to a powerful strategy for automatically annotating glomeruli in kidney biopsy images. However, since any artificial intelligence can make mistakes, strategies for identifying and correcting faulty annotations are often indispensable. Yet, how can such a validation be achieved without the laborious task of a pathologist manually checking every single image? To address this issue, the current project performed an extensive study on the use of shape analysis to automatically evaluate the glomerular annotations produced by deep-learning segmentation. Examining a large repertoire of shape descriptors on over 168000 glomerular predictions, the study found that morphometry could successfully highlight and distinguish between three different types of segmentation inconsistencies. In addition, using shape descriptors to rank segmentation annotations, it was possible to obtain a distinct enrichment of errors on the leading edge of the ranking, implying that pathologists would only have to inspect and correct the most suspicious fraction of all annotations. Ultimately, the study suggested a panel of three shape descriptors that enabled an efficient enrichment of all errors, respective or irrespective of error type. In summary, the work demonstrates the methodological aspects and benefits of shape analysis for evaluating glomerular segmentation results. We are convinced that, by applying such a strategy for detecting segmentation errors, it will be possible to approach a more time-efficient correction of deep learning-derived glomerular annotations.

pathology↗

bioRxiv2025

GlomExtractor: a versatile tool for extracting glomerular patches from whole slide kidney biopsy images

The extraction of glomerular image patches is a key step prior to the training and application of glomerular classification models. Numerous algorithms for detecting and segmenting glomeruli have already been described in the literature, but standards for how to extract glomeruli from such an initial annotation remain lacking. Furthermore, the impact of different choices in extracting and preprocessing, such as cropping and scaling, are poorly understood and researched. To address this gap, the current paper introduces the GlomExtractor, a versatile tool implementing the key steps for extracting glomerular patches from whole slide images, including optional filtering of segmentations, patch extraction and scaling, and postprocessing such as background removal. By utilizing the GlomExtractor in combination with glomerular clustering experiments, the current study demonstrated how different processing strategies can impact the performance of downstream machine learning applications. We believe that the GlomExtractor and the experiments demonstrated in the current paper will help researchers in (i) the development of glomerular classification models, in (ii) advancing our understanding of the impact of patch processing on model performance, and in (iii) establishing community standards for how glomeruli should be extracted depending on downstream use.

pathology↗

bioRxiv2024

Unsupervised learning for labeling global glomerulosclerosis

Current deep learning models for classifying glomeruli in nephropathology are trained almost exclusively in a supervised manner, requiring expert-labeled images. Very little is known about the potential for unsupervised learning to overcome this bottleneck. To address this open question in a proof-of-concept, the project focused on the most fundamental classification task: globally sclerosed versus non-globally sclerosed glomeruli. The performance of clustering between the two classes was extensively studied across a variety of labeled datasets with diverse compositions and histological stains, and across the feature embeddings produced by 34 different pre-trained CNN models. As demonstrated by the study, clustering of globally and non-globally sclerosed glomeruli is generally highly feasible, yielding accuracies of over 95% in most datasets. Further work will be required to expand these experiments towards the clustering of additional glomerular lesion categories. We are convinced that these efforts (i) will open up opportunities for semi-automatic labeling approaches, thus alleviating the need for labor-intensive manual labeling, and (ii) illustrate that glomerular classification models can potentially be trained even in the absence of expert-derived class labels.

pathology↗