{"cells":[{"metadata":{"papermill":{"duration":0.006161,"end_time":"2020-11-06T17:21:49.514533","exception":false,"start_time":"2020-11-06T17:21:49.508372","status":"completed"},"tags":[]},"cell_type":"markdown","source":"# Prior Work on Glomeruli Detection in Microscopy Images\nby Leah Scherschel - Indiana University"},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","collapsed":true,"papermill":{"duration":0.006578,"end_time":"2020-11-06T17:21:49.526573","exception":false,"start_time":"2020-11-06T17:21:49.519995","status":"completed"},"tags":[]},"cell_type":"markdown","source":"## Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning (Sheehan and Korstanje 2018)\n\n**Abstract:** Current methods of scoring histological kidney samples, specifically glomeruli, do not allow for collection of quantitative data in a high-throughput and consistent manner. Neither untrained individuals nor computers are presently capable of identifying glomerular features, so expert pathologists must do the identification and score using a categorical matrix, complicating statistical analysis. Critical information regarding overall health and physiology is encoded in these samples. Rapid comprehensive histological scoring could be used, in combination with other physiological measures, to significantly advance renal research. Therefore, we used machine learning to develop a high-throughput method to automatically identify and collect quantitative data from glomeruli. Our method requires minimal human interaction between steps and provides quantifiable data independent of user bias. The method uses free existing software and is usable without extensive image analysis training. Validation of the classifier and feature scores in mice is highlighted in this work and shows the power of applying this method in murine research. Preliminary results indicate that the method can be applied to data sets from different species after training on relevant data, allowing for fast glomerular identification and quantitative measurements of glomerular features. Validation of the classifier and feature scores are highlighted in this work and show the power of applying this method. The resulting data are free from user bias. Continuous data, such that statistical analysis can be performed, allows for more precise and comprehensive interrogation of samples. These data can then be combined with other physiological data to broaden our overall understanding of renal function.\n\n**Reference:** Sheehan, Susan M., and Ron Korstanje. 2018. “Automatic Glomerular Identification and Quantification of Histological Phenotypes Using Image Analysis and Machine Learning.” American Journal of Physiology - Renal Physiology 315 (6): F1644–51. https://doi.org/10.1152/ajprenal.00629.2017."},{"metadata":{"papermill":{"duration":0.004935,"end_time":"2020-11-06T17:21:49.536574","exception":false,"start_time":"2020-11-06T17:21:49.531639","status":"completed"},"tags":[]},"cell_type":"markdown","source":"## Glomerulus Classification and Detection Based on Convolutional Neural Networks (Gallego et al. 2018)\n\n**Abstract:** 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.\n\n**Reference:** Gallego, Jaime, Anibal Pedraza, Samuel Lopez, Georg Steiner, Lucia Gonzalez, Arvydas Laurinavicius, and Gloria Bueno. 2018. “Glomerulus Classification and Detection Based on Convolutional Neural Networks.” Journal of Imaging 4 (1): 20. https://doi.org/10.3390/jimaging4010020."},{"metadata":{"papermill":{"duration":0.004651,"end_time":"2020-11-06T17:21:49.546342","exception":false,"start_time":"2020-11-06T17:21:49.541691","status":"completed"},"tags":[]},"cell_type":"markdown","source":"## Glomerular detection and segmentation from multimodal microscopy images using a Butterworth band-pass filter (Govind et al. 2018)\n\n**Abstract:** We present a rapid, scalable, and high throughput computational pipeline to accurately detect and segment the glomerulus from renal histopathology images with high precision and accuracy. Our proposed method integrates information from fluorescence and bright-field microscopy imaging of renal tissues. For computation, we exploit the simplicity, yet extreme robustness of Butterworth bandpass filter to extract the glomeruli by utilizing the information inherent in the renal tissue stained with immunofluorescence marker sensitive at blue emission wavelength as well as tissue auto-fluorescence. The resulting output is in-turn used to detect and segment multiple glomeruli within the fieldof-view in the same tissue section post-stained with histopathological stains. Our approach, optimized over 40 images, produced a sensitivity/specificity of 0.95/0.84 on n = 66 test images, each containing one or more glomeruli. The work not only has implications in renal histopathology involving diseases with glomerular structural damages, which is vital to track the progression of the disease, but also aids in the development of a tool to rapidly generate a database of glomeruli from whole slide images, essential for training neural networks. The current practice to detect glomerular structural damage is by the manual examination of biopsied renal tissues, which is laborious, time intensive and tedious. Existing automated pipelines employ complex neural networks which are computationally extensive, demand expensive highperformance hardware and require large expert-annotated datasets for training. Our automated method to detect glomerular boundary will aid in rapid extraction of glomerular compartmental features from large renal histopathological images.\n\n**Reference:** Govind, Darshana, Brandon Ginley, Brendon Lutnick, John E. Tomaszewski, and Pinaki Sarder. 2018. “Glomerular Detection and Segmentation from Multimodal Microscopy Images Using a Butterworth Band-Pass Filter.” In Medical Imaging 2018: Digital Pathology, 10581:1058114. International Society for Optics and Photonics. https://doi.org/10.1117/12.2295446."},{"metadata":{"papermill":{"duration":0.004672,"end_time":"2020-11-06T17:21:49.556095","exception":false,"start_time":"2020-11-06T17:21:49.551423","status":"completed"},"tags":[]},"cell_type":"markdown","source":"## Region-Based Convolutional Neural Nets for Localization of Glomeruli in Trichrome-Stained Whole Kidney Sections (Bukowy et al. 2018)\n\n**Abstract:** *Background* Histologic examination of fixed renal tissue is widely used to assess morphology and the progression of disease. Commonly reported metrics include glomerular number and injury. However, characterization of renal histology is a time-consuming and user-dependent process. To accelerate and improve the process, we have developed a glomerular localization pipeline for trichrome-stained kidney sections using a machine learning image classification algorithm.\n\n*Methods* We prepared 4-μm slices of kidneys from rats of various genetic backgrounds that were subjected to different experimental protocols and mounted the slices on glass slides. All sections used in this analysis were trichrome stained and imaged in bright field at a minimum resolution of 0.92 μm per pixel. The training and test datasets for the algorithm comprised 74 and 13 whole renal sections, respectively, totaling over 28,000 glomeruli manually localized. Additionally, because this localizer will be ultimately used for automated assessment of glomerular injury, we assessed bias of the localizer for preferentially identifying healthy or damaged glomeruli.\n\n*Results* Localizer performance achieved an average precision and recall of 96.94% and 96.79%, respectively, on whole kidney sections without evidence of bias for or against glomerular injury or the need for manual preprocessing.\n\n*Conclusions* This study presents a novel and robust application of convolutional neural nets for the localization of glomeruli in healthy and damaged trichrome-stained whole-renal section mounts and lays the groundwork for automated glomerular injury scoring.\n\n**Reference:** Bukowy, John D., Alex Dayton, Dustin Cloutier, Anna D. Manis, Alexander Staruschenko, Julian H. Lombard, Leah C. Solberg Woods, Daniel A. Beard, and Allen W. Cowley. 2018. “Region-Based Convolutional Neural Nets for Localization of Glomeruli in Trichrome-Stained Whole Kidney Sections.” Journal of the American Society of Nephrology 29 (8): 2081–88. https://doi.org/10.1681/ASN.2017111210."},{"metadata":{"papermill":{"duration":0.004655,"end_time":"2020-11-06T17:21:49.565714","exception":false,"start_time":"2020-11-06T17:21:49.561059","status":"completed"},"tags":[]},"cell_type":"markdown","source":"## Segmentation of Glomeruli Within Trichrome Images Using Deep Learning (Kannan et al. 2019)\n\n**Abstract:** *Introduction* The number of glomeruli and glomerulosclerosis evaluated on kidney biopsy slides constitute standard components of a renal pathology report. Prevailing methods for glomerular assessment remain manual, labor intensive, and nonstandardized. We developed a deep learning framework to accurately identify and segment glomeruli from digitized images of human kidney biopsies.\n*Methods* Trichrome-stained images (n = 275) from renal biopsies of 171 patients with chronic kidney disease treated at the Boston Medical Center from 2009 to 2012 were analyzed. A sliding window operation was defined to crop each original image to smaller images. Each cropped image was then evaluated by at least 3 experts into 3 categories: (i) no glomerulus, (ii) normal or partially sclerosed (NPS) glomerulus, and (iii) globally sclerosed (GS) glomerulus. This led to identification of 751 unique images representing nonglomerular regions, 611 images with NPS glomeruli, and 134 images with GS glomeruli. A convolutional neural network (CNN) was trained with cropped images as inputs and corresponding labels as output. Using this model, an image processing routine was developed to scan the test images to segment the GS glomeruli.\n*Results* The CNN model was able to accurately discriminate nonglomerular images from NPS and GS images (performance on test data: Accuracy: 92.67% ± 2.02% and Kappa: 0.8681 ± 0.0392). The segmentation model that was based on the CNN multilabel classifier accurately marked the GS glomeruli on the test data (Matthews correlation coefficient = 0.628).\n*Conclusion* This work demonstrates the power of deep learning for assessing complex histologic structures from digitized human kidney biopsies.\n\n**Reference:** Kannan, Shruti, Laura A. Morgan, Benjamin Liang, McKenzie G. Cheung, Christopher Q. Lin, Dan Mun, Ralph G. Nader, et al. 2019. “Segmentation of Glomeruli Within Trichrome Images Using Deep Learning.” Kidney International Reports 4 (7): 955–62. https://doi.org/10.1016/j.ekir.2019.04.008.\n"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}