SVIA dataset: A new dataset of microscopic videos and images for computer-aided sperm analysis

• Autorzy: Chen Ao , Li Chen , Zou Shuojia , Rahaman Md Mamunur , Yao Yudong , Chen Haoyuan , Yang Hechen , Zhao Peng , Hu Weiming , Liu Wanli , Grzegorzek Marcin

Identyfikatory

DOI

10.1016/j.bbe.2021.12.010

• Języki publikacji: EN

Abstrakty

EN

Computer-Aided Sperm Analysis (CASA) is a widely studied topic in the diagnosis and treatment of male reproductive health. Although CASA has been evolving, there is still a lack of publicly available large-scale image datasets for CASA. To fill this gap, we provide the Sperm Videos and Images Analysis (SVIA) dataset, including three different subsets, subset-A, subset-B and subset-C, to test and evaluate different computer vision techniques in CASA. For subset-A, in order to test and evaluate the effectiveness of SVIA dataset for object detection, we use five representative object detection models and four commonly used evaluation metrics. For subset-B, in order to test and evaluate the effectiveness of SVIA dataset for image segmentation, we used eight representative methods and three standard evaluation metrics. Moreover, to test and evaluate the effectiveness of SVIA dataset for object tracking, we have employed the traditional kNN with progressive sperm (PR) as an evaluation metric and two deep learning models with three standard evaluation metrics. For subset-C, to prove the effectiveness of SVIA dataset for image denoising, nine denoising filters are used to denoise thirteen kinds of noise, and the mean structural similarity is calculated for evaluation. At the same time, to test and evaluate the effectiveness of SVIA dataset for image classification, we evaluate the results of twelve convolutional neural network models and six visual transformer models using four commonly used evaluation metrics. Through a series of experimental analyses and comparisons in this paper, it can be concluded that this proposed dataset can evaluate not only the functions of object detection, image segmentation, object tracking, image denoising, and image classification but also the robustness of object detection and image classification models. Therefore, SVIA dataset can fill the gap of the lack of large-scale public datasets in CASA and promote the development of CASA. Dataset is available at: .https://github.com/Demozsj/Detection-Sperm.

Słowa kluczowe

EN

open dataset; medical image; microscopic image; microscopic video; computer aided sperm analysis

PL

zbiór danych; obraz medyczny; obraz mikroskopowy; analiza nasienia; wspomaganie komputerowe

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 1
• Strony: 204--214
• Opis fizyczny: Bibliogr. 52 poz., rys., tab.

Twórcy

autor

Chen Ao

• Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

autor

Li Chen

• Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China

autor

Zou Shuojia

• Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

autor

Rahaman Md Mamunur

• Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

autor

Yao Yudong

• Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA

autor

Chen Haoyuan

• Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

autor

Yang Hechen

• Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

autor

Zhao Peng

• Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

autor

Hu Weiming

• Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

autor

Liu Wanli

• Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

autor

Grzegorzek Marcin

• Institute of Medical Informatics, University of Luebeck, Luebeck, Germany

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