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3D lung segmentation of the CT series based on 2D Chan-Vese

Maciura Łukasz, Rosa Wojciech, Wójcik Dariusz, Rymarczyk Tomasz, Maj Michał, Kozłowski Edward

Przegląd Elektrotechniczny

2022

R. 98, nr 5

165--168

This paper presents a new 3D segmentation algorithm for lung segmentation tasks on CT series. The algorithm consists of a 2D stage (for each slice) which is performed parallelly and 3D postprocessing after merging to 3D. The 2D stage consists of 2D preprocessing, Chan - Vese segmentation, and 2D postprocessing. This algorithm was tested on the set of 60 CT series containing labelled data enable to its assessment. The results of the algorithm are close to deep learning approaches. This algorithm will be an element of a commercial expert system for medical applications where some patient assessment will be necessary based on segmented human organs.

Ten artykuł prezentuje nowy algorytm segmentacji 3D do zadań segmentacji płuc na seriach z tomografii komputerowej. Ten algorytm składa się z etapu 2D (dla każdego przekroju) który jest wykonywany równolegle i post-processingu 3D po scaleniu wyników do 3D. Etap 2D składa się z pre-processingu 2D, segmentacji Chan – Vese I post-processingu 2D. Algorytm był przetestowany na zbiorze 60 serii obtazów z tomografii komputerowej zawierających zaetykietowane dane co umożliwiło jego ocenę. Wyniki algorytmu są przybliżonej dokładności do rozwiązań deep learning. Algorytm ten będzie elementem komercyjnego system ekspertowego do zastosowań medycznych, gdzie niezbędna będzie ocena pacienta bazując na segmentowanych organach człowieka.

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

Pandey Mohit, Gupta Abhishek

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 4

1601--1628

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.