Saad Ahmed Sirohey

Model-Based analysis of local shape for lesion detection in CT scans

Thin-slice computer tomography provides high-resolution images that facilitate the diagnosis of early-stage lung cancer. However, the sheer size of the CT volumes introduces variability in radiological readings, driving the need for automated detection systems. The main contribution of this paper is a technique for combining geometric and intensity models with the analysis of local curvature for detecting pulmonary lesions in CT. The local shape at each voxel is represented via the principal curvatures of its associated isosurface without explicitly extracting the isosurface. The comparison of these curvatures to values derived from analytical shape models is then used to label the voxel as belonging to particular anatomical structures, e.g., nodules or vessels. The algorithm was evaluated on 242 CT exams with expert-determined ground truth. The performance of the algorithm is quantified by free-response receiver-operator characteristic curves, as well as by its potential for improvement in radiologist sensitivity.

Part-Based local shape models for colon polyp detection

This paper presents a model-based technique for lesion detection in colon CT scans that uses analytical shape models to map the local shape curvature at individual voxels to anatomical labels. Local intensity profiles and curvature information have been previously used for discriminating between simple geometric shapes such as spherical and cylindrical structures. This paper introduces novel analytical shape models for colon-specific anatomy, viz. folds and polyps, built by combining parts with simpler geometric shapes. The models better approximate the actual shapes of relevant anatomical structures while allowing the application of model-based analysis on the simpler model parts. All parameters are derived from the analytical models, resulting in a simple voxel labeling scheme for classifying individual voxels in a CT volume. The algorithms performance is evaluated against expert-determined ground truth on a database of 42 scans and performance is quantified by free-response receiver-operator curves.

A probabilistic model for haustral curvatures with applications to colon CAD

Among the many features used for classification in computer-aided detection (CAD) systems targeting colonic polyps, those based on differences between the shapes of polyps and folds are most common. We introduce here an explicit parametric model for the haustra or colon wall. The proposed model captures the overall shape of the haustra and we use it to derive the probability distribution of features relevant to polyp detection. The usefulness of the model is demonstrated through its application to a colon CAD algorithm.

Quantitative measurement of MR cortical atrophy: MR brain surface intensity model (BSIM) and group and individual cortical thinning studies

Alzheimer’s disease (AD) is caused by pathological changes including cortical thinning occurring throughout the brain. Traditional methods for assessing cortical thickness are challenged by the sub-millimeter accuracy required for clinical conditions and the convoluted nature of brain surface. Furthermore, there is a significant overlap of gray and white matter intensities. A novel Brain Surface Intensity Model (BSIM) has been developed for use as a potential imaging biomarker for neurodegenerative diseases. BSIM technique extracts MR intensity profiles perpendicular to a mathematically defined gray matter iso-intensity layer (GMIIL) at predefined reference points, fits that profile to BSIM, and computes cortical thickness. A 3D visualization tool has been developed to evaluate intensity extraction and model calculation. 29 normal subjects aged between 70 to 80 years from ADNI database were used to generate normal references and measure individual Z-score cortical thinning. 30 age-matched AD subjects were used to study thinning patterns. Significant cortical thinning (p < 0.0001) was found for AD group. 95% confidence interval of the cortical thinning in AD patients was from 0.17 to 0.23 mm. The cortical thinning of the AD patients showed distinct features that differentiate AD patients from normal controls. The thickness measurements of 29 normal controls were validated by comparing with results from literature (p = 0.94). BSIM technique avoids complicated 3D segmentation of brain gray and white matters, and simplifies the thickness calculation. Moreover, it is less affected by the image noise, inhomogeneity, partial volume effects, and the intensity overlap of the white and gray matters.