Detection of glomeruli in renal pathology by mutual comparison of multiple staining modalities

Temerinac-Ott, Maja; Forestier, Germain; Schmitz, Jessica; Hermsen, Meyke; Braseni, J.H.; Feuerhake, Friedrich; Wemmert, Cédric

doi:10.1109/ispa.2017.8073562

Cited by 41 publications

(32 citation statements)

References 18 publications

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“…In [24], the authors evaluate the detection of glomerular structures in WSIs stained with multiple histochemical staining using HOG feature classifier and CNN with a specific model. They compare the performance of both methods for each stain, and the combination of the detection in consecutive sections stained with different histochemical staining.…”

Section: Previous Workmentioning

confidence: 99%

Glomerulus Classification and Detection Based on Convolutional Neural Networks

et al. 2018

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Glomerulus classification and detection in kidney tissue segments are key processes in nephropathology used for the correct diagnosis of the diseases. In this paper, we deal with the challenge of automating Glomerulus classification and detection from digitized kidney slide segments using a deep learning framework. The proposed method applies Convolutional Neural Networks (CNNs) between two classes: Glomerulus and Non-Glomerulus, to detect the image segments belonging to Glomerulus regions. We configure the CNN with the public pre-trained AlexNet model and adapt it to our system by learning from Glomerulus and Non-Glomerulus regions extracted from training slides. Once the model is trained, labeling is performed by applying the CNN classification to the image blocks under analysis. The results of the method indicate that this technique is suitable for correct Glomerulus detection in Whole Slide Images (WSI), showing robustness while reducing false positive and false negative detections.

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Section: Previous Workmentioning

confidence: 99%

Glomerulus Classification and Detection Based on Convolutional Neural Networks

et al. 2018

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“…Some recent studies employed mainly CNN for glomerular detection. In [23], the authors evaluated the difference in performance between the HOG with the SVM classifier and the three-layered CNN classifier for glomerular detection in WSIs with multiple stains. They employed the sliding window approach to scan the WSIs.…”

Section: Convolutional Neural Network (Cnn) Based Methodsmentioning

confidence: 99%

Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images

et al. 2018

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The detection of objects of interest in high-resolution digital pathological images is a key part of diagnosis and is a labor-intensive task for pathologists. In this paper, we describe a Faster R-CNN-based approach for the detection of glomeruli in multistained whole slide images (WSIs) of human renal tissue sections. Faster R-CNN is a state-of-the-art general object detection method based on a convolutional neural network, which simultaneously proposes object bounds and objectness scores at each point in an image. The method takes an image obtained from a WSI with a sliding window and classifies and localizes every glomerulus in the image by drawing the bounding boxes. We configured Faster R-CNN with a pretrained Inception-ResNet model and retrained it to be adapted to our task, then evaluated it based on a large dataset consisting of more than 33,000 annotated glomeruli obtained from 800 WSIs. The results showed the approach produces comparable or higher than average F-measures with different stains compared to other recently published approaches. This approach could have practical application in hospitals and laboratories for the quantitative analysis of glomeruli in WSIs and, potentially, lead to a better understanding of chronic glomerulonephritis.

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“…Proof-of-concept classifiers adapted from both the AlexNet [39] and GoogleNet [10] models were shown to be able to differentiate image patches containing isolated normal glomeruli from non-glomerular structures [24]. CNNs were also demonstrated to outperform HOG classifiers in glomerulus detection accuracy when applied to random image patches from kidney WSI [25]. Additional promising results were demonstrated by cascading the output of one CNN (optimized for glomerulus detection in downsampled WSI) to another (adapted for precise segmentation at higher resolutions) [23].…”

Section: Related Workmentioning

confidence: 99%

“…The remainder describe differences in descriptors in glomeruli from normal and pathologic populations, rather than isolating and labeling different glomeruli within the same wide image field. The current study [27] WSI, anti-desmin, fixed rat segmentation (normal, diabetic) [28] field, anti-nestin, fixed rat segmentation, characterization (normal, renal failure) [23] WSI, PAS, fixed mouse segmentation (normal) [25] WSI, Jones H&E/PAS/other, fixed human segmentation (normal) [22] WSI, H&E/PAS/others human, mouse, rat segmentation, characterization (normal, diabetic) [29] WSI, H&E primate segmentation, characterization (normal, diseased) [30] WSI, Masson's trichrome human segmentation - [31] field, H&E mouse segmentation - [24] patch, PAS human segmentation - [32] patch rat segmentation, characterization (normal, hypertrophy) [33] patch, H&E/PAS, fixed human segmentation, characterization (normal, proliferating) [34], [35] patch, H&E/PAS, fixed mouse, rat segmentation, characterization (normal) [36]- [38] patch -segmentation demonstrates the novel use of CNNs applied to frozen H&E sections to detect non-sclerotic and sclerotic glomeruli to assist pathologists in intra-operative interpretation of percent global glomerulosclerosis.…”

Section: Related Workmentioning

confidence: 99%

“…In the realm of histopathology, CNNs have been most commonly applied to cancer detection and classification [15]- [21]. More recently, several studies have employed CNNs for glomerulus identification in renal biopsies [22]- [25]. However, none of these studies describes the use of frozen sections as input to detection algorithms, nor are they capable of differentiating normal from sclerotic glomeruli after detection without the use of special stains.…”

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Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

Marsh

Matlock

Kudose

et al. 2018

Preprint

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Transplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intraoperative donor kidney biopsies is essential for determining which kidneys are eligible for transplantation. The criteria for accepting or rejecting donor kidneys relies heavily on pathologist determination of the percent of glomeruli (determined from a frozen section) that are normal and sclerotic. This percentage is a critical measurement that correlates with transplant outcome. Inter-and intra-observer variability in donor biopsy evaluation is, however, significant. An automated method for determination of percent global glomerulosclerosis could prove useful in decreasing evaluation variability, increasing throughput, and easing the burden on pathologists. Here, we describe the development of a deep learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. This model extends a convolutional neural network (CNN) pre-trained on a large database of digital images. The extended model, when trained on just 48 whole slide images, exhibits slide-level evaluation performance on par with expert renal pathologists. The model substantially outperforms a model trained on image patches of isolated glomeruli. Encouragingly, the model's performance is robust to slide preparation artifacts associated with frozen section preparation. As the first model reported that identifies and classifies normal and sclerotic glomeruli in frozen kidney sections, and thus the first model reported in the literature relevant to kidney transplantation, it may become an essential part of donor kidney biopsy evaluation in the clinical setting.

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Detection of glomeruli in renal pathology by mutual comparison of multiple staining modalities

Cited by 41 publications

References 18 publications

Glomerulus Classification and Detection Based on Convolutional Neural Networks

Glomerulus Classification and Detection Based on Convolutional Neural Networks

Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

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