Analysis of deep learning approaches for automated prostate segmentation: literature review

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Abstract

Background. Delineation of the prostate boundaries represents the initial step in understanding the state of the whole organ and is mainly manually performed, which takes a long time and directly depends on the experience of the radiologists. Automated prostate selection can be carried out by various approaches, including using artificial intelligence and its subdisciplines – machine and deep learning.
Aim. To reveal the most accurate deep learning-based methods for prostate segmentation on multiparametric magnetic resonance images.
Materials and methods. The search was conducted in July 2022 in the PubMed database with a special clinical query (((AI) OR (machine learning)) OR (deep learning)) AND (prostate) AND (MRI). The inclusion criteria were availability of the full article, publication date no more than five years prior to the time of the search, availability of a quantitative assessment of the reconstruction accuracy by the Dice similarity coefficient (DSC) calculation.
Results. The search returned 521 articles, but only 24 papers including descriptions of 33 different deep learning networks for prostate segmentation were selected for the final review. The median number of cases included for artificial intelligence training was 100 with a range from 25 to 365. The optimal DSC value threshold (0.9), in which automated segmentation is only slightly inferior to manual delineation, was achieved in 21 studies.
Conclusion. Despite significant achievements in the development of deep learning-based prostate segmentation algorithms, there are still problems and limitations that should be resolved before artificial intelligence can be implemented in clinical practice.

About the authors

A. E. Talyshinskii

Med-Ray

Email: ali-ma@mail.ru
ORCID iD: 0000-0002-3521-8937

 Ali El’manovich Talyshinskii 

Build. 1, 11 Serebryakova Proezd, Moscow 129343, Russia 

Russian Federation

B. G. Guliev

I.I. Mechnikov North-West State Medical University, Ministry of Health of Russia;
Mariinsky Hospital

Author for correspondence.
Email: gulievbg@mail.ru
ORCID iD: 0000-0002-2359-6973

41 Kirochnaya St., Saint Petersburg 191015, Russia

56 Liteynyy Prospekt, Saint Petersburg 191014, Russia 

Russian Federation

I. G. Kamyshanskaya

Med-Ray;
Mariinsky Hospital;
Saint Petersburg State University

Email: irinaka@mail.ru
ORCID iD: 0000-0002-8351-9216

Build. 1, 11 Serebryakova Proezd, Moscow 129343, Russia 

56 Liteynyy Prospekt, Saint Petersburg 191014, Russia

7–9 Universitetskaya Naberezhnaya, Saint Petersburg 199034, Russia 

Russian Federation

A. I. Novikov

I.I. Mechnikov North-West State Medical University, Ministry of Health of Russia;
N.P. Napalkov Saint Petersburg Clinical Scientific and Practical Center for Specialized Types of Medical Care (Oncological)

Email: novikov_urol@mail.ru
ORCID iD: 0000-0003-4359-979X

41 Kirochnaya St., Saint Petersburg 191015, Russia 

lit. A, 68A Leningradskaya St., Pesochnyy, Saint Petersburg 197758, Russia 

Russian Federation

U. Zhanbyrbekuly

Astana Medical University

Email: Ulanbek.amu@gmail.com
ORCID iD: 0000-0003-1849-6924

Department of Urology and Andrology

49A Beybitshilik St., Astana 010000, Republic of Kazakhstan 

Kazakhstan

A. E. Mamedov

Samara University

Email: anar555mamedov@gmail.com
ORCID iD: 0000-0002-6800-4505

34 Moskovskoye Shosse, Samara 443086, Russia 

Russian Federation

I. A. Povago

I.I. Mechnikov North-West State Medical University, Ministry of Health of Russia

Email: eetwo@yandex.ru
ORCID iD: 0000-0002-3145-0245

41 Kirochnaya St., Saint Petersburg 191015, Russia 

Russian Federation

A. A. Andriyanov

I.I. Mechnikov North-West State Medical University, Ministry of Health of Russia

Email: mr.haisenberg001@gmail.com
ORCID iD: 0000-0001-6905-0581

41 Kirochnaya St., Saint Petersburg 191015, Russia 

Russian Federation

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