Evidence map›Paper›PMID 33676102›Full record

ArticleMedical image analysis2021

Computerized spermatogenesis staging (CSS) of mouse testis sections via quantitative histomorphological analysis.

Jun Xu, Haoda Lu, Haixin Li, Chaoyang Yan, Xiangxue Wang, Min Zang, Dirk G de Rooij, Anant Madabhushi, Eugene Yujun Xu

Open access · greenAbstract read
In one paragraph

Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
4.6field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 45 citations in OpenAlex.

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  19. Proliferation and Apoptosis of Cat (Veterinary sciences · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 6 institutions in 3 countries.

Jun XuJiangsu Key Laboratory of Big Data Analysis Technique and CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China. Electronic address: xujung@gmail.com.
Haoda LuJiangsu Key Laboratory of Big Data Analysis Technique and CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Haixin LiState Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, China.
Chaoyang YanJiangsu Key Laboratory of Big Data Analysis Technique and CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Xiangxue WangDepartment of Biomedical Engineering, Case Western Reserve University, OH 44106-7207, USA.
Min ZangState Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, China.
Dirk G de RooijReproductive Biology Group, Division of Developmental Biology, Dept. of Biology, Faculty of Science, Utrecht University, Utrecht 3584 CH, The Netherlands; Center for Reproductive Medicine, Academic Medical Center, University of Amsterdam, Amsterdam 1105 AZ, The Netherlands.
Anant MadabhushiDepartment of Biomedical Engineering, Case Western Reserve University, OH 44106-7207, USA; Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio 44106-7207, USA.
Eugene Yujun XuState Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, China; Department of Neurology, Center for Reproductive Sciences, Northwestern University Feinberg School of Medicine, IL 60611, USA. Electronic address: e-xu@northwestern.edu.
Nanjing University of Information Science and Technology · CNNanjing Medical University · CNCase Western Reserve University · USLouis Stokes Cleveland VA Medical Center · USNorthwestern University · USUtrecht University · NL

Funding

Computerized histologic image predictor of cancer outcomeR01CA202752 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI FELDMAN, MICHAEL D, GANESAN, SHRIDAR · 2016 to 2020
$3.1M
Quantitative Histomorphometric Risk Classifier (QuHbIC) in HPV + Oropharyngeal CarcinomaR01CA220581 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI KOYFMAN, SHLOMO, LEWIS, JAMES · 2018 to 2023
$3.1M
Computerized Histologic Risk Predictor (CHiRP) for Early Stage Lung CancersR01CA216579 · NCI · EMORY UNIVERSITY · PI FU, PINGFU, LLOYD, MARK · 2018 to 2023
$3.1M
MR Fingerprinting and Computerized Decision Support for Prostate CancerR01CA208236 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GULANI, VIKAS, PONSKY, LEE EVAN · 2017 to 2022
$3.0M
Pathology Image Informatics Platform for visualization, analysis and managementU24CA199374 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI GURCAN, METIN NAFI, MADABHUSHI, ANANT · 2015 to 2019
$2.9M
RadxTools for assessing tumor treatment response on imagingU01CA248226 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI TIWARI, PALLAVI, VISWANATH, SATISH EASWAR · 2020 to 2022
$1.4M
HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identificationU01CA239055 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI JANOWCZYK, ANDREW ROBERT, MADABHUSHI, ANANT · 2019 to 2022
$1.0M
A Study to Validate and Improve an Automated Image Analysis Algorithm to Detect Tuberculosis in Sputum Smear SlidesR43EB028736 · NIBIB · DIASCOPIC, LLC · PI MADABHUSHI, ANANT, SERIF, CARY · 2019 to 2019
$225k
RadIO: A Novel Radiomics Toolkit to Predict and Characterize Response to Immunotherapy in Stage III Non-Small Cell Lung CancerI01BX004121 · VA · VETERANS HEALTH ADMINISTRATION · PI Anant Madabhushi, Charles J Nock · 2019 to 2026
–
BIOMEDICAL ENGINEERING RESEARCH FACILITIESC06RR012463 · NCRR · CASE WESTERN RESERVE UNIVERSITY · PI KUTINA, KENNETH L · 1997 to 1997
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BLRD VA I01 BX004121BLRD VA IK6 BX006185NCI NIH HHS R01 CA202752NCI NIH HHS R01 CA208236NCI NIH HHS R01 CA216579NCI NIH HHS R01 CA220581NCI NIH HHS U01 CA239055NCI NIH HHS U01 CA248226NCI NIH HHS U24 CA199374NCRR NIH HHS C06 RR012463NIBIB NIH HHS R43 EB028736
6 · The paper itself

Abstract

Spermatogenesis in mammals is a cyclic process of spermatogenic cell development in the seminiferous epithelium that can be subdivided into 12 subsequent stages. Histological staging analysis of testis sections, specifically of seminiferous tubule cross-sections, is the only effective method to evaluate the quality of the spermatogenic process and to determine developmental defects leading to infertility. Such staging analysis, however, is tedious and time-consuming, and it may take a long time to become proficient. We now have developed a Computerized Staging system of Spermatogenesis (CSS) for mouse testis sections through learning of an expert with decades of experience in mouse testis staging. The development of the CSS system comprised three major parts: 1) Developing computational image analysis models for mouse testis sections; 2) Automated classification of each seminiferous tubule cross-section into three stage groups: Early Stages (ES: stages I-V), Middle Stages (MS: stages VI-VIII), and Late Stages (LS: stages IV-XII); 3) Automated classification of MS into distinct stages VI, VII-mVIII, and late VIII based on newly developed histomorphological features. A cohort of 40 H&E stained normal mouse testis sections was built according to three modules where 28 cross-sections were leveraged for developing tubule region segmentation, spermatogenic cells types and multi-concentric-layers segmentation models. The rest of 12 testis cross-sections, approximately 2314 tubules whose stages were manually annotated by two expert testis histologists, served as the basis for developing the CSS system. The CSS system's accuracy of mean and standard deviation (MSD) in identifying ES, MS, and LS were 0.93 ± 0.03, 0.94 ± 0.11, and 0.89 ± 0.05 and 0.85 ± 0.12, 0.88 ± 0.07, and 0.96 ± 0.04 for one with 5 years of experience, respectively. The CSS system's accuracy of MSD in identifying stages VI, VII-mVIII, and late VIII are 0.74 ± 0.03, 0.85 ± 0.04, and 0.78 ± 0.06 and 0.34 ± 0.18, 0.78 ± 0.16, and 0.44 ± 0.25 for one with 5 years of experience, respectively. In terms of time it takes to collect these data, it takes on average 3 hours for a histologist and 1.87 hours for the CSS system to finish evaluating an entire testis section (computed with a PC (I7-6800k 4.0 GHzwith 32GB of RAM & 256G SSD) and a Titan 1080Ti GPU). Therefore, the CSS system is more accurate and faster compared to a human histologist in staging, and further optimization and development will not only lead to a complete staging of all 12 stages of mouse spermatogenesis but also could aid in the future diagnosis of human infertility. Moreover, the top-ranking histomorphological features identified by the CSS classifier are consistent with the primary features used by histologists in discriminating stages VI, VII-mVIII, and late VIII.

Indexed as

SpermatogenesisTestisAnimalsMaleMiceSeminiferous EpitheliumSeminiferous TubulesComputerized staging of spermatogenesisDeep learningMouse testicular section imagesMouse testis histologySeminiferous tubulesSpermatogenic cell segmentationSperm development

Identifiers

PMID33676102
PMCPMC8046964
OpenAlexW3091957562

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