Juxta-Vascular Pulmonary Nodule Segmentation in PET-CT Imaging Based on an LBF Active Contour Model with Information Entropy and Joint Vector

The accurate segmentation of pulmonary nodules is an important preprocessing step in computer-aided diagnoses of lung cancers. However, the existing segmentation methods may cause the problem of edge leakage and cannot segment juxta-vascular pulmonary nodules accurately. To address this problem, a novel automatic segmentation method based on an LBF active contour model with information entropy and joint vector is proposed in this paper. Our method extracts the interest area of pulmonary nodules by a standard uptake value (SUV) in Positron Emission Tomography (PET) images, and automatic threshold iteration is used to construct an initial contour roughly. The SUV information entropy and the gray-value joint vector of Positron Emission Tomography–Computed Tomography (PET-CT) images are calculated to drive the evolution of contour curve. At the edge of pulmonary nodules, evolution will be stopped and accurate results of pulmonary nodule segmentation can be obtained. Experimental results show that our method can achieve 92.35% average dice similarity coefficient, 2.19 mm Hausdorff distance, and 3.33% false positive with the manual segmentation results. Compared with the existing methods, our proposed method that segments juxta-vascular pulmonary nodules in PET-CT images is more accurate and efficient

Software Environment for Modeling and Quantification of Distribution of Radiopharmaceuticals for Needs of Nuclear Medicine

Diplomová práce shrnuje základní poznatky z oblasti nukleární medicíny a radiační ochrany a zabývá se návrhem modelu pro automatickou segmentaci sleziny z obrazů PET/CT. Cílem této práce je extrakce jasových intenzit z jednotlivých řezů PET a na základě statistické analýzy kvantifikovat míru distribuce radiofarmaka ve slezině. Jako možné řešení pro přesnou extrakci sleziny ze snímků PET se nabízí vytvoření CT binární masky segmentovaného objektu zájmu a její přenesení na řezy PET. Segmentační metoda založená na aktivních konturách je testována v prostředí MATLAB na padesáti probandech. Pro zajištění kvalitního procesu segmentační metody je nutno data nejprve převést na stejnou velikost matice, zredukovat obrazy pouze na oblast zájmu a provést multimodální registraci. Následně je možno na takto upravené snímky aplikovat metodu založenou na aktivních konturách, která probíhá paralelně ve všech řezech, v obou modalitách. Kvantifikace manifestace radioindikátoru vychází ze studie Radiomics a je provedena prostřednictvím statistické analýzy zahrnující výpočet průměrné hodnoty intenzity pixelů v jednotlivých řezech, stanovení maxima a minima intenzit a směrodatnou odchylku.The diploma thesis summarizes the fundamentals from the field of nuclear medicine and radiation protection, and deals with the design of a model for automatic segmentation of the spleen from PET/CT images. The aim of this work is to extract brightness intensities from individual PET frames and, based on statistical analysis, to quantify the degree of distribution of radiopharmaceuticals in the spleen. A possible solution for accurate extraction of the spleen from PET images is to create a CT binary mask of a segmented object of interest and transfer it to original PET frames. The segmentation method based on active contours is tested in the MATLAB environment on fifty probands. To ensure a high-quality segmentation process, it is necessary to convert input data to the same matrix size, reduce the whole dataset to the region of interest only, and perform multimodal registration. Subsequently, it is possible to apply a method based on active contours to such modified images, which runs collaterally in all sections of both modalities at once. The quantification of the radioindicator manifestation is based on the Radiomics study and is carried out through statistical analysis including the calculation of the average value of the pixel intensity in individual frames, the determination of the maximum and minimum intensities, and the standard deviation.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn