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PL
W artykule przedstawiono algorytmy opisu kształtu, które mogą zostać wykorzystane do budowy wiedzy a priori, o którą można wzbogacić metody segmentacji danych medycznych. Opisana metodologia została wykorzystana do analizy kształtu struktur anatomicznych okolicy miednicy. Przeprowadzona analiza pozwoliła sprawdzić zmienność geometrii struktur anatomicznych istotnych z punktu widzenia radioterapii nowotworu prostaty, Zmienność kształtu organów oceniono zarówno: pomiędzy osobami w populacji chorych z nowotworem gruczołu krokowego jak i zmienność tych kształtów podczas procesu radioterapeutycznego u pacjenta.
EN
Prostate cancer is one of most frequently diagnosed cancer diseases among men population, especially in Europe and the USA. The number of fatal cases is also significant. It leads to many attempts to improve processes of the cancer diagnosis and therapy. One of most promising methods of treatment is radiation therapy. However, its proper planning requires contouring of every important structure on every slice obtained from the imaging equipment (in example a CT scanner), which is time-consuming for medical staff. To solve this problem, many efforts are made to construct algorithms of automatic segmentation of organs in 3D data. To provide the expected efficiency of such methods, a base of a priori knowledge about organs to be delineated is desired. In this paper we present shape description algorithms which could be used to collect the a priori knowledge, potentially able to improve the medical data segmentation methods. The described methodology was used in shape analysis of pelvic region structures, important for planning the prostate cancer radiation therapy, which included: GTV (Gross Tumor Volume), rectum, bladder and femoral heads. In this paper 5 different algorithms are presented. The first proposed method describes the shape of the analyzed organ with parameters (semi-axis lengths) of minimum-volume ellipsoid circumscribed on the structure. The other algorithms provide the information about the shape of the analyzedstructure as a distribution of chosen geometric quantity values (such as distance) between the groups of points randomly selected on its surface. The proposed algorithms were tested on the organ models reconstructed from the structures contoured on the images obtained from CT. As a result of the performed analysis, geometrical variability of the considered structures were specified. Variability of shapes of the analyzed organs was examined for the patients from the population group of men with diagnosed prostate cancer as well as for the single patient cases during radiation therapy.
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