Deep learning for cell image segmentation and ranking

Araújo, Flávio H. D.; Silva, Romuere; Ushizima, Daniela; Rezende, Mariana; Carneiro, Cláudia Martins; Bianchi, Andrea Gomes Campos; Medeiros, Fátima N. S. de

doi:10.1016/j.compmedimag.2019.01.003

Cited by 99 publications

(61 citation statements)

References 26 publications

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“…When using only the Pap test as a screening method, the percentage of samples with lesions is 3% to 10% 104‐107 . On the other hand, the combination of hrHPV test, AM, and Pap test allows increasing the number of samples with lesions to 50% to 85%, 101,102 which increases the representativeness of pre‐relevant neoplasms for the algorithm, making it more sensitive 108,109 . Thus, the FNRs caused by AM are likely to decrease.…”

Section: Discussionmentioning

confidence: 99%

Cervical cancer: Automation of Pap test screening

Rezende

Bianchi

Carneiro

2021

Diagnostic Cytopathology

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Background Cervical cancer progresses slowly, increasing the chance of early detection of pre‐neoplastic lesions via Pap exam test and subsequently preventing deaths. However, the exam presents both false‐negatives and false‐positives results. Therefore, automatic methods (AMs) of reading the Pap test have been used to improve the quality control of the exam. We performed a literature review to evaluate the feasibility of implementing AMs in laboratories. Methods This work reviewed scientific publications regarding automated cytology from the last 15 years. The terms used were “Papanicolaou test” and “Automated cytology screening” in Portuguese, English, and Spanish, in the three scientific databases (SCIELO, PUBMED, MEDLINE). Results Of the resulting 787 articles, 34 were selected for a complete review, including three AMs: ThinPrep Imaging System, FocalPoint GS Imaging System and CytoProcessor. In total, 1 317 148 cytopathological slides were evaluated automatically, with 1 308 028 (99.3%) liquid‐based cytology slides and 9120 (0.7%) conventional cytology smears. The AM diagnostic performances were statistically equal to or better than those of the manual method. AM use increased the detection of cellular abnormalities and reduced false‐negatives. The average sample rejection rate was ≤3.5%. Conclusion AMs are relevant in quality control during the analytical phase of cervical cancer screening. This technology eliminates slide‐handling steps and reduces the sample space, allowing professionals to focus on diagnostic interpretation while maintaining high‐level care, which can reduce false‐negatives. Further studies with conventional cytology are needed. The use of AM is still not so widespread in cytopathology laboratories.

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Section: Discussionmentioning

confidence: 99%

Cervical cancer: Automation of Pap test screening

Rezende

Bianchi

Carneiro

2021

Diagnostic Cytopathology

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“…One of the most ubiquitous tasks in bioimage analysis is the partitioning of images into meaningful segments for downstream quantification and statistical evaluation [17] , [19] , [26] . It is therefore no surprise that the bulk of literature on deep learning in many application areas of computer vision including bioimage analysis has focused on the potential for image segmentation [188] , [190] , [219] , [220] , [221] , [222] . Similar to object detection, image segmentation can be cast as a classification problem, this time down to the pixel level rather than the object level, which indeed is the approach taken by many deep-learning based methods.…”

Section: Deep Learning For Bioimage Analysismentioning

confidence: 99%

A bird’s-eye view of deep learning in bioimage analysis

Meijering

2020

Computational and Structural Biotechnology Journal

115

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Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird’s-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.

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“…In [111], the author suggests a deep learning algorithm based on LeNet architecture to segment both free-lying and overlapping clumps of the abnormal cell from conventional digitized pap smear images and rank those images according to their level of abnormalities. A preprocessing system eliminates images before segmentation if it only holds background or inadequate information, which improves the computational cost.…”

Section: A Reference Reviewmentioning

confidence: 99%

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Rahaman

et al. 2020

IEEE Access

101

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Cervical cancer is one of the most common and deadliest cancers among women. Despite that, this cancer is entirely treatable if it is detected at a precancerous stage. Pap smear test is the most extensively performed screening method for early detection of cervical cancer. However, this hand-operated screening approach suffers from a high false-positive result because of human errors. To improve the accuracy and manual screening practice, computer-aided diagnosis methods based on deep learning is developed widely to segment and classify the cervical cytology images automatically. In this survey, we provide a comprehensive study of the state of the art approaches based on deep learning for the analysis of cervical cytology images. Firstly, we introduce deep learning and its simplified architectures that have been used in this field. Secondly, we discuss the publicly available cervical cytopathology datasets and evaluation metrics for segmentation and classification tasks. Then, a thorough review of the recent development of deep learning for the segmentation and classification of cervical cytology images is presented. Finally, we investigate the existing methodology along with the most suitable techniques for the analysis of pap smear cells.

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Deep learning for cell image segmentation and ranking

Cited by 99 publications

References 26 publications

Cervical cancer: Automation of Pap test screening

Cervical cancer: Automation of Pap test screening

A bird’s-eye view of deep learning in bioimage analysis

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

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