scholarly journals Deep learning segmentation of glomeruli on kidney donor frozen sections

2021 ◽  
Vol 8 (06) ◽  
Author(s):  
Xiang Li ◽  
Richard C. Davis ◽  
Yuemei Xu ◽  
Zehan Wang ◽  
Nao Souma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Frozen Sections ◽  
Kidney Donor

scholarly journals Deep learning quantification of percent steatosis in donor liver biopsy frozen sections

EBioMedicine ◽  
2020 ◽  
Vol 60 ◽  
pp. 103029
Author(s):  
Lulu Sun ◽  
Jon N. Marsh ◽  
Matthew K. Matlock ◽  
Ling Chen ◽  
Joseph P. Gaut ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Biopsy ◽  
Frozen Sections ◽  
Donor Liver

scholarly journals On the acceptance of “fake” histopathology: A study on frozen sections optimized with deep learning

2022 ◽  
Vol 13 (1) ◽  
pp. 6
Author(s):  
Michael Gadermayr ◽  
Mario Siller ◽  
LeaMaria Stangassinger ◽  
Christina Kreutzer ◽  
Peter Boor ◽  
...  
Keyword(s):  
Deep Learning ◽  
Frozen Sections

Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning

2020 ◽  
Vol 108 ◽  
pp. 101918
Author(s):  
Yuan Li ◽  
Pingjun Chen ◽  
Zhiyuan Li ◽  
Hai Su ◽  
Lin Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Frozen Sections ◽  
Rule Based ◽  
Automatic Diagnosis

scholarly journals Deep Learning Segmentation of Glomeruli on Kidney Donor Frozen Sections

2021 ◽  
Author(s):  
Richard C. Davis ◽  
Xiang Li ◽  
Yuemei Xu ◽  
Zehan Wang ◽  
Nao Souma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Standard Of Care ◽  
Dice Similarity Coefficient ◽  
Manual Segmentation ◽  
Frozen Sections ◽  
Donor Kidney ◽  
Automatic Quantification ◽  
Testing Dataset ◽  
Computational Image Analysis ◽  
Whole Slide Images

Purpose: Recent advances in computational image analysis offer the opportunity to develop automatic quantification of histologic parameters as aid tools for practicing pathologists. This work aims to develop deep learning (DL) models to quantify non-sclerotic and sclerotic glomeruli on frozen sections from donor kidney biopsies. Approach: A total of 258 whole slide images (WSI) from cadaveric donor kidney biopsies performed at our institution (n=123) and at external institutions (n=135) were used in this study. WSIs from our institution were divided at the patient level into training and validation datasets (Ratio: 0.8:0.2) and external WSIs were used as an independent testing dataset. Non-sclerotic (n=22767) and sclerotic (n=1366) glomeruli were manually annotated by study pathologists on all WSIs. A 9-layer convolutional neural network based on the common U-Net architecture was developed and tested for the segmentation of non-sclerotic and sclerotic glomeruli. DL-derived, manual segmentation and reported glomerular count (standard of care) were compared. Results: The average Dice Similarity Coefficient testing was 0.90 and 0.83. and the F1, Recall, and Precision scores were 0.93, 0.96, and 0.90, and 0.87, 0.93, and 0.81, for non-sclerotic and sclerotic glomeruli, respectively. DL-derived and manual segmentation derived glomerular counts were comparable, but statistically different from reported glomerular count. Conclusions: DL segmentation is a feasible and robust approach for automatic quantification of glomeruli. This work represents the first step toward new protocols for the evaluation of donor kidney biopsies.

scholarly journals Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

2018 ◽  
Vol 37 (12) ◽  
pp. 2718-2728 ◽  
Author(s):  
Jon N. Marsh ◽  
Matthew K. Matlock ◽  
Satoru Kudose ◽  
Ta-Chiang Liu ◽  
Thaddeus S. Stappenbeck ◽  
...  
Keyword(s):  
Deep Learning ◽  
Frozen Sections ◽  
Transplant Kidney ◽  
Global Glomerulosclerosis

scholarly journals 247 – Deep Learning Algorithm Accurately Predicts Percent Steatosis in Donor Liver Biopsy Frozen Sections

2019 ◽  
Vol 156 (6) ◽  
pp. S-1186-S-1187
Author(s):  
Lulu Sun ◽  
Jon Marsh ◽  
Elizabeth M. Brunt ◽  
Sanjay J. Swamidass ◽  
Ta-Chiang Liu
Keyword(s):  
Deep Learning ◽  
Liver Biopsy ◽  
Learning Algorithm ◽  
Frozen Sections ◽  
Donor Liver ◽  
Deep Learning Algorithm

Immunoferritin localization of intracellular antigens in positively stained frozen sections

1978 ◽  
Vol 36 (2) ◽  
pp. 168-169
Author(s):  
K. T. Tokuyasu
Keyword(s):  
Surface Tension ◽  
Water Surface ◽  
Thin Layers ◽  
The Other ◽  
Positive Staining ◽  
Frozen Sections ◽  
The Past ◽  
Ultrathin Frozen Sections ◽  
Definition Of

During the past investigations of immunoferritin localization of intracellular antigens in ultrathin frozen sections, we found that the degree of negative staining required to delineate u1trastructural details was often too dense for the recognition of ferritin particles. The quality of positive staining of ultrathin frozen sections, on the other hand, has generally been far inferior to that attainable in conventional plastic embedded sections, particularly in the definition of membranes. As we discussed before, a main cause of this difficulty seemed to be the vulnerability of frozen sections to the damaging effects of air-water surface tension at the time of drying of the sections.Indeed, we found that the quality of positive staining is greatly improved when positively stained frozen sections are protected against the effects of surface tension by embedding them in thin layers of mechanically stable materials at the time of drying (unpublished).

Localization of pancreatic enzymes in ultrathin frozen sections stained with metal labeled antibody

1978 ◽  
Vol 36 (2) ◽  
pp. 90-91
Author(s):  
Kenjiro Yasuda
Keyword(s):  
Fluorescent Antibody ◽  
Pancreatic Enzymes ◽  
Tissue Preparation ◽  
Zymogen Granules ◽  
Frozen Sections ◽  
En Bloc ◽  
Antibody Method ◽  
Ultrathin Frozen Sections ◽  
Fluorescent Antibody Method

Localization of amylase,chymotrypsinogen and trypsinogen in pancreas was demonstrated by Yasuda and Coons (1966), by using fluorescent antibody method. These enzymes were naturally found in the zymogen granules. Among them, amylase showed a diffuse localization around the nucleus, in addition to the zymogen granules. Using ferritin antibody method, scattered ferritin granules were also found around the Golgi area (Yasuda et al.,1967). The recent advance in the tissue preparation enables the antigen to be localized in the ultrathin frozen sections, by applying the labeled antibodies onto the sections instead of staining the tissue en bloc.The present study deals with the comparison of the localization of amylase and lipase demonstrated by applying the bismuth-labeled, peroxidase-labeled and ferritin-labeled antibody methods on the ultrathin frozen sections of pancreas, and on the blocks of the same tissue.

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