Joint Probabilistic Model of Shape and Intensity for Multiple Abdominal Organ Segmentation From Volumetric CT Images
We propose a novel joint probabilistic model that correlates a new probabilistic shape model with the corresponding global intensity distribution to segment multiple abdominal organs simultaneously. Our probabilistic shape model estimates the probability of an individual voxel belonging to the estimated shape of the object. The probability density of the estimated shape is derived from a combination of the shape variations of target class and the observed shape information. To better capture the shape variations, we used probabilistic principle component analysis optimized by expectation maximization to capture the shape variations and reduce computational complexity. The maximum a posteriori estimation was optimized by the iterated conditional mode-expectation maximization. We used 72 training datasets including low-and high-contrast computed tomography images to construct the shape models for the liver, spleen, and both kidneys. We evaluated our algorithm on 40 test datasets that were grouped into normal (34 normal cases) and pathologic (six datasets) classes. The testing datasets were from different databases and manual segmentation was performed by different clinicians. We measured the volumetric overlap percentage error, relative volume difference, average square symmetric surface distance, false positive rate, and false negative rate and our method achieved accurate and robust segmentation for multiple abdominal organs simultaneously.
computed tomography (ct) expectation maximization (em) image segmentation probabilistic principle component analysis (pca)