Using cine magnetic resonance imaging (cine MRI) images to track cardiac motion helps users to analyze the myocardial strain, and is of great importance in clinical applications. At present, most of the automatic deep learning-based motion tracking methods compare two images without considering temporal information between MRI frames, which easily leads to the lack of consistency of the generated motion fields. Even though a small number of works take into account the temporal factor, they are usually computationally intensive or have limitations on image length.
View Article and Find Full Text PDF. Sliding motion may occur between organs in anatomical regions due to respiratory motion and heart beating. This issue is often neglected in previous studies, resulting in poor image registration performance.
View Article and Find Full Text PDFPurpose: Automatic liver segmentation from computed tomography (CT) images is an essential preprocessing step for computer-aided diagnosis of liver diseases. However, due to the large differences in liver shapes, low-contrast to adjacent tissues, and existence of tumors or other abnormalities, liver segmentation has been very challenging. This study presents an accurate and fast liver segmentation method based on a novel probabilistic active contour (PAC) model and its fast global minimization scheme (3D-FGMPAC), which is explainable as compared with deep learning methods.
View Article and Find Full Text PDFThis paper presents an automatic Couinaud segmentation method based on deep learning of key point detection. Assuming that the liver mask has been extracted, the proposed method can automatically divide the liver into eight anatomical segments according to Couinaud's definition. Firstly, an attentive residual hourglass-based cascaded network (ARH-CNet) is proposed to identify six key bifurcation points of the hepatic vascular system.
View Article and Find Full Text PDFPurpose: Deformable image registration (DIR) of lung four-dimensional computed tomography (4DCT) plays a vital role in a wide range of clinical applications. Most of the existing deep learning-based lung 4DCT DIR methods focus on pairwise registration which aims to register two images with large deformation. However, the temporal continuities of deformation fields between phases are ignored.
View Article and Find Full Text PDFThis paper presents an automatic lobe-based labeling of airway tree method, which can detect the bifurcation points for reconstructing and labeling the airway tree from a computed tomography image. A deep learning-based network structure is designed to identify the four key bifurcation points. Then, based on the detected bifurcation points, the entire airway tree is reconstructed by a new region-growing method.
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