A systematic review of the automatic kidney segmentation methods in abdominal images

• Autorzy: Pandey Mohit , Gupta Abhishek

Identyfikatory

DOI

10.1016/j.bbe.2021.10.006

• Języki publikacji: EN

Abstrakty

EN

Background and Purpose: The precise kidney segmentation is very helpful for diagnosis and treatment planning in urology, by giving information about malformation in the shape and size of the kidney. Kidney segmentation in abdominal computed tomography (CT) images provides support for the efficient and effortless detection of kidney tumors or cancers. Manual kidney segmentation is time-consuming and not reproducible. To overcome this problem, computer-aided automatic approach is used for kidney segmentation. The purpose of presenting this review paper is to analyze different automatic kidney segmentation methods in abdominal CT scans. Materials and Methods: PRISMA guidelines were used to conduct the systematic review. To acquire related articles, three online open source databases were used and a query was formed with relevant keywords. On the basis of inclusion and exclusion criteria, relevant papers were selected from the search results for finding answers to the four evolved research questions. Results: The results reported in the different studies were analyzed based on the formulated research questions. The challenges of these studies were listed to overcome in the future. Many performance parameters representing the results like Hausdorff Distance (HD) and Dice Similarity Coefficient (DSC) were compared among the relevant studies. Conclusion: The systematic review article consists of the essence of the several computer-aided kidney segmentation methods using abdominal CT images, which are dedicated to answering the evolved research questions like various methods, accuracy, datasets size, various challenges, and the effect of pathological kidney on the performance of segmentation method had been discussed.

Słowa kluczowe

EN

CT segmentation; kidney segmentation; deep learning framework; volumetric segmentation; automatic kidney segmentation

PL

segmentacja CT; segmentacja nerek; uczenie głębokie; segmentacja wolumetryczna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 4
• Strony: 1601--1628
• Opis fizyczny: Bibliogr. 139 poz., rys., tab., wykr.

Twórcy

autor

Pandey Mohit

• School of Computer Science & Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra, Jammu & Kashmir, India

autor

Gupta Abhishek

• School of Computer Science & Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra, 182320, Jammu & Kashmir, India

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