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.

Absence of CD34 on murine skeletal muscle satellite cells marks a reversible state of activation during acute injury.

BACKGROUND: Skeletal muscle satellite cells are myogenic progenitors that reside on myofiber surface beneath the basal lamina. In recent years satellite cells have been identified and isolated based on their expression of CD34, a sialomucin surface receptor traditionally used as a marker of hematopoietic stem cells. Interestingly, a minority of satellite cells lacking CD34 has been described. METHODOLOGY/PRINCIPAL FINDINGS: In order to elucidate the relationship between CD34+ and CD34- satellite cells we utilized fluorescence-activated cell sorting (FACS) to isolate each population for molecular analysis, culture and transplantation studies. Here we show that unless used in combination with alpha7 integrin, CD34 alone is inadequate for purifying satellite cells. Furthermore, the absence of CD34 marks a reversible state of activation dependent on muscle injury. CONCLUSIONS/SIGNIFICANCE: Following acute injury CD34- cells become the major myogenic population whereas the percentage of CD34+ cells remains constant. In turn activated CD34- cells can reverse their activation to maintain the pool of CD34+ reserve cells. Such activation switching and maintenance of reserve pool suggests the satellite cell compartment is tightly regulated during muscle regeneration.

Toward early diagnosis of lung cancer.

Our long term research goal is to develop a fully automated, image-based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. In this paper, we focus on generating new probabilistic models for the estimated growth rate of the detected lung nodules from Low Dose Computed Tomography (LDCT). We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global target-to-prototype alignment of one scan to another using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate relative deformations. Visual appearance of these chest images is described using a Markov-Gibbs random field (MGRF) model with multiple pairwise interaction. An affine transformation that globally registers a target to a prototype is estimated by the gradient ascent-based maximization of a special Gibbs energy function. To handle local deformations, we displace each voxel of the target over evolving closed equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by a speed function in the directions that minimize distances between the corresponding voxel pairs on the iso-surfaces in both the data sets. Preliminary results show that the proposed accurate registration could lead to precise diagnosis and identification of the development of the detected pulmonary nodules.