Ahmed Elnakib

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patients chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

Focal cortical dysplasias in autism spectrum disorders

BackgroundPrevious reports indicate the presence of histological abnormalities in the brains of individuals with autism spectrum disorders (ASD) suggestive of a dysplastic process. In this study we identified areas of abnormal cortical thinning within the cerebral cortex of ASD individuals and examined the same for neuronal morphometric abnormalities by using computerized image analysis.ResultsThe study analyzed celloidin-embedded and Nissl-stained serial full coronal brain sections of 7 autistic (ADI-R diagnosed) and 7 age/sex-matched neurotypicals. Sections were scanned and manually segmented before implementing an algorithm using Laplace’s equation to measure cortical width. Identified areas were then subjected to analysis for neuronal morphometry. Results of our study indicate the presence within our ASD population of circumscribed foci of diminished cortical width that varied among affected individuals both in terms of location and overall size with the frontal lobes being particularly involved. Spatial statistic indicated a reduction in size of neurons within affected areas. Granulometry confirmed the presence of smaller pyramidal cells and suggested a concomitant reduction in the total number of interneurons.ConclusionsThe neuropathology is consistent with a diagnosis of focal cortical dysplasia (FCD). Results from the medical literature (e.g., heterotopias) and our own study suggest that the genesis of this cortical malformation seemingly resides in the heterochronic divisions of periventricular germinal cells. The end result is that during corticogenesis radially migrating neuroblasts (future pyramidal cells) are desynchronized in their development from those that follow a tangential route (interneurons). The possible presence of a pathological mechanism in common among different conditions expressing an autism-like phenotype argue in favor of considering ASD a “sequence” rather than a syndrome. Focal cortical dysplasias in ASD may serve to explain the high prevalence of seizures and sensory abnormalities in this patient population.

Precise Segmentation of 3-D Magnetic Resonance Angiography

Accurate automatic extraction of a 3-D cerebrovascular system from images obtained by time-of-flight (TOF) or phase contrast (PC) magnetic resonance angiography (MRA) is a challenging segmentation problem due to the small size objects of interest (blood vessels) in each 2-D MRA slice and complex surrounding anatomical structures (e.g., fat, bones, or gray and white brain matter). We show that due to the multimodal nature of MRA data, blood vessels can be accurately separated from the background in each slice using a voxel-wise classification based on precisely identified probability models of voxel intensities. To identify the models, an empirical marginal probability distribution of intensities is closely approximated with a linear combination of discrete Gaussians (LCDG) with alternate signs, using our previous EM-based techniques for precise linear combination of Gaussian-approximation adapted to deal with the LCDGs. The high accuracy of the proposed approach is experimentally validated on 85 real MRA datasets (50 TOF and 35 PC) as well as on synthetic MRA data for special 3-D geometrical phantoms of known shapes.

Medical Image Segmentation: A Brief Survey

Accurate segmentation of 2-D, 3-D, and 4-D medical images to isolate anatomical objects of interest for analysis is essential in almost any computer-aided diagnosis system or other medical imaging applications. Various aspects of segmentation features and algorithms have been extensively explored for many years in a host of publications. However, the problem remains challenging, with no general and unique solution, due to a large and constantly growing number of different objects of interest, large variations of their properties in images, different medical imaging modalities, and associated changes of signal homogeneity, variability, and noise for each object. This chapter overviews most popular medical image segmentation techniques and discusses their capabilities, and basic advantages and limitations. The state-of-the-art techniques of the last decade are also outlined.

Quantitative analysis of the shape of the corpus callosum in patients with autism and comparison individuals

Multiple studies suggest that the corpus callosum in patients with autism is reduced in size. This study attempts to elucidate the nature of this morphometric abnormality by analyzing the shape of this structure in 17 high-functioning patients with autism and an equal number of comparison participants matched for age, sex, IQ, and handedness. The corpus callosum was segmented from T1 weighted images acquired with a Siemens 1.5 T scanner. Transformed coordinates of the curvilinear axis were aggregated into a parametric map and compared across series to derive regions of statistical significance. Our results indicate that a reduction in size of the corpus callosum occurs over all of its subdivisions (genu, body, splenium) in patients with autism. Since the commissural fibers that traverse the different anatomical compartments of the corpus callosum originate in disparate brain regions our results suggest the presence of widely distributed cortical abnormalities in people with autism.

3d kidney segmentation from CT images using a level set approach guided by a novel stochastic speed function

Kidney segmentation is a key step in developing any noninvasive computer-aided diagnosis (CAD) system for early detection of acute renal rejection. This paper describes a new 3-D segmentation approach for the kidney from computed tomography (CT) images. The kidney borders are segmented from the surrounding abdominal tissues with a geometric deformable model guided by a special stochastic speed relationship. The latter accounts for a shape prior and appearance features in terms of voxel-wise image intensities and their pair-wise spatial interactions integrated into a two-level joint Markov-Gibbs random field (MGRF) model of the kidney and its background. The segmentation approach was evaluated on 21 CT data sets with available manual expert segmentation. The performance evaluation based on the receiver operating characteristic (ROC) and Dice similarity coefficient (DSC) between manually drawn and automatically segmented contours confirm the robustness and accuracy of the proposed segmentation approach.

Accurate Automated Detection of Autism Related Corpus Callosum Abnormalities

The importance of accurate early diagnostics of autism that severely affects personal behavior and communication skills cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the Corpus Callosum (CC) in autistic brains. We propose a new approach to quantitative analysis of three-dimensional (3D) magnetic resonance images (MRI) of the brain that ensures a more accurate quantification of anatomical differences between the CC of autistic and normal subjects. It consists of three main processing steps: (i) segmenting the CC from a given 3D MRI using the learned CC shape and visual appearance; (ii) extracting a centerline of the CC; and (iii) cylindrical mapping of the CC surface for its comparative analysis. Our experiments revealed significant differences (at the 95% confidence level) between 17 normal and 17 autistic subjects in four anatomical divisions, i.e. splenium, rostrum, genu and body of their CCs.

Effects of Physiologic Mechanical Stimulation on Embryonic Chick Cardiomyocytes Using a Microfluidic Cardiac Cell Culture Model

Hemodynamic mechanical cues play a critical role in the early development and functional maturation of cardiomyocytes (CM). Therefore, tissue engineering approaches that incorporate immature CM into functional cardiac tissues capable of recovering or replacing damaged cardiac muscle require physiologically relevant environments to provide the appropriate mechanical cues. The goal of this work is to better understand the subcellular responses of immature cardiomyocytes using an in vitro cardiac cell culture model that realistically mimics in vivo mechanical conditions, including cyclical fluid flows, chamber pressures, and tissue strains that could be experienced by implanted cardiac tissues. Cardiomyocytes were cultured in a novel microfluidic cardiac cell culture model (CCCM) to achieve accurate replication of the mechanical cues experienced by ventricular CM. Day 10 chick embryonic ventricular CM (3.5 × 104 cell clusters per cell chamber) were cultured for 4 days in the CCCM under cyclic mechanical stimulation (10 mmHg, 8–15% stretch, 2 Hz frequency) and ventricular cells from the same embryo were cultured in a static condition for 4 days as controls. Additionally, ventricular cell suspensions and ventricular tissue from day 16 chick embryo were collected and analyzed for comparison with CCCM cultured CM. The gene expressions and protein synthesis of calcium handling proteins decreased significantly during the isolation process. Mechanical stimulation of the cultured CM using the CCCM resulted in an augmentation of gene expression and protein synthesis of calcium handling proteins compared to the 2D constructs cultured in the static conditions. Further, the CCCM conditioned 2D constructs have a higher beat rate and contractility response to isoproterenol. These results demonstrate that early mechanical stimulation of embryonic cardiac tissue is necessary for tissue proliferation and for protein synthesis of the calcium handling constituents required for tissue contractility. Thus, physiologic mechanical conditioning may be essential for generating functional cardiac patches for replacement of injured cardiac tissue.

3D shape analysis for early diagnosis of malignant lung nodules

An alternative method of diagnosing malignant lung nodules by their shape, rather than conventional growth rate, is proposed. The 3D surfaces of the detected lung nodules are delineated by spherical harmonic analysis that represents a 3D surface of the lung nodule supported by the unit sphere with a linear combination of special basis functions, called Spherical Harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D lung nodule segmentation with a deformable 3D boundary controlled by a new prior visual appearance model; (ii) 3D Delaunay triangulation to construct a 3D mesh model of the segmented lung nodule surface; (iii) mapping this model to the unit sphere; (iv) computing the SHs for the surface; and (v) determining the number of the SHs to delineate the lung nodule. We describe the lung nodule shape complexity with a new shape index, the estimated number of the SHs, and use it for the K-nearest classification into malignant and benign lung nodules. Preliminary experiments on 327 lung nodules (153 malignant and 174 benign) resulted in a classification accuracy of 93.6%, showing that the proposed method is a promising supplement to current technologies for the early diagnosis of lung cancer.

Kidney segmentation using graph cuts and pixel connectivity

Kidney segmentation from abdominal MRI data is used as an effective and accurate indicator for renal function in many clinical situations. The goal of this research is to accurately segment kidney from very low contrast MRI data. The present problem becomes challenging mainly due to poor contrast, high noise and partial volume effects introduced during the scanning process. In this paper, we propose a novel kidney segmentation algorithm using graph cuts and pixel connectivity. A connectivity term is introduced in the energy function of the standard graph cut via pixel labeling. Each pixel is assigned a different label based on its probabilities to belong to two different segmentation classes and probabilities of its neighbors to belong to these segmentation classes. The labeling process is formulated according to Dijkstras shortest path algorithm. Experimental results yield a (mean+/-s.d.) Dice coefficient value of (98.60+/-0.52)% on 25 datasets.