Joint Probabilistic Model of Shape and Intensity for Multiple Abdominal Organ Segmentation From Volumetric CT Images

IEEE Journal of Biomedical and Health Informatics (Impact Factor: 1.44). 11/2013; 17(1):92-102. DOI: 10.1109/TITB.2012.2227273
Source: PubMed


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 CT 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 (6 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.

17 Reads
  • [Show abstract] [Hide abstract]
    ABSTRACT: We propose a framework that efficiently employs intensity, gradient, and textural features for three-dimensional (3-D) segmentation of medical (MRI/CT) volumes. Our methodology commences by determining the magnitude of intensity variations across the input volume using a 3-D gradient detection scheme. The resultant gradient volume is utilized in a dynamic volume growing/formation process that is initiated in voxel locations with small gradient magnitudes and is concluded at sites with large gradient magnitudes, yielding a map comprising an initial set of partitions (or subvolumes). This partition map is combined with an entropy-based texture descriptor along with intensity and gradient attributes in a multivariate analysis-based volume merging procedure that fuses subvolumes with similar characteristics to yield a final/refined segmentation output. Additionally, a semiautomated version of the aforestated algorithm that allows a user to interactively segment a desired subvolume of interest as opposed to the entire volume is also discussed. Our approach was tested on several MRI and CT datasets and the results show favorable performance in comparison to the state-of-the-art ITK-SNAP technique.
    No preview · Article · May 2015
  • [Show abstract] [Hide abstract]
    ABSTRACT: To develop a novel automated method for segmentation of the injured spleen using morphological properties following abdominal trauma. Average attenuation of a normal spleen in computed tomography (CT) does not vary significantly between subjects. However, in the case of solid organ injury, the shape and attenuation of the spleen on CT may vary depending on the time and severity of the injury. Timely assessment of the severity and extent of the injury is of vital importance in the setting of trauma. We developed an automated computer-aided method for segmenting the injured spleen from CT scans of patients who had splenectomy due to abdominal trauma. We used ten subjects to train our computer-aided diagnosis (CAD) method. To validate the CAD method, we used twenty subjects in our testing group. Probabilistic atlases of the spleens were created using manually segmented data from ten CT scans. The organ location was modeled based on the position of the spleen with respect to the left side of the spine followed by the extraction of shape features. We performed the spleen segmentation in three steps. First, we created a mask of the spleen, and then we used this mask to segment the spleen. The third and final step was the estimation of the spleen edges in the presence of an injury such as laceration or hematoma. The traumatized spleens were segmented with a high degree of agreement with the radiologist-drawn contours. The spleen quantification led to [Formula: see text] volume overlap, [Formula: see text] Dice similarity index, [Formula: see text] precision/sensitivity, [Formula: see text] volume estimation error rate, [Formula: see text] average surface distance/root-mean-squared error. Our CAD method robustly segments the spleen in the presence of morphological changes such as laceration, contusion, pseudoaneurysm, active bleeding, periorgan and parenchymal hematoma, including subcapsular hematoma due to abdominal trauma. CAD of the splenic injury due to abdominal trauma can assist in rapid diagnosis and assessment and guide clinical management. Our segmentation method is a general framework that can be adapted to segment other injured solid abdominal organs.
    No preview · Article · Sep 2015 · International Journal of Computer Assisted Radiology and Surgery
  • [Show abstract] [Hide abstract]
    ABSTRACT: Purpose: Propose a fully automatic 3D segmentation framework to segment liver on challenging cases that contain the low contrast of adjacent organs and the presence of pathologies from abdominal CT images. Methods: First, all of the atlases are weighted in the selected training datasets by calculating the similarities between the atlases and the test image to dynamically generate a subject-specific probabilistic atlas for the test image. The most likely liver region of the test image is further determined based on the generated atlas. A rough segmentation is obtained by a maximum a posteriori classification of probability map, and the final liver segmentation is produced by a shape-intensity prior level set in the most likely liver region. Our method is evaluated and demonstrated on 25 test CT datasets from our partner site, and its results are compared with two state-of-the-art liver segmentation methods. Moreover, our performance results on 10 MICCAI test datasets are submitted to the organizers for comparison with the other automatic algorithms. Results: Using the 25 test CT datasets, average symmetric surface distance is [Formula: see text] mm (range 0.62-2.12 mm), root mean square symmetric surface distance error is [Formula: see text] mm (range 0.97-3.01 mm), and maximum symmetric surface distance error is [Formula: see text] mm (range 12.73-26.67 mm) by our method. Our method on 10 MICCAI test data sets ranks 10th in all the 47 automatic algorithms on the site as of July 2015. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our method is a promising tool to improve the efficiency of both techniques. Conclusion: The applicability of the proposed method to some challenging clinical problems and the segmentation of the liver are demonstrated with good results on both quantitative and qualitative experimentations. This study suggests that the proposed framework can be good enough to replace the time-consuming and tedious slice-by-slice manual segmentation approach.
    No preview · Article · Dec 2015 · International Journal of Computer Assisted Radiology and Surgery