Automatic organ localization plays a significant role in medical image segmentation. This paper introduces a novel approach for simultaneous and automatic two surface detection of renal cortex from contrast enhanced abdominal CT scans. The proposed framework is an integrated procedure consisting of three main parts: (i) cortex model training, both two shape variabilities are detected using principal components analysis from the manual annotation, and dual shape dictionaries and appearance dictionaries are constructed; (ii) outer mesh reconstruction, the initialized outer mesh is iteratively deformed to the target boundary; (iii) inner mesh reconstruction, the inner mesh can be reconstructed using the same deformation coefficients and similarity transformation of the outer mesh with the inner mesh shape dictionary. Our method was validated on a clinical data set of 37 CT scans using the leave-one-out cross validation strategy. The proposed method has improved the overall segmentation accuracy of Dice similarity coefficient to 91.95%±3.15% for renal cortex segmentation.
Accurate lung segmentation is of great significance in clinical application. However, it is still a challenging task due to its complex structures, pathological changes, individual differences and low image quality. In the paper, a novel shape dictionary-based approach, named active shape dictionary, is introduced to automatically delineate pathological lungs from clinical 3D CT images. The active shape dictionary improves sparse shape composition in eigenvector space to effectively reduce local shape reconstruction error. The proposed framework makes the shape model to be iteratively deformed to target boundary with discriminative appearance dictionary learning and gradient vector flow to drive the landmarks. The proposed algorithm is tested on 40 3D low-dose CT images with lung tumors. Compared to state-of-the-art methods, the proposed approach can robustly and accurately detect pathological lung surface.
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