3D functional models of monkey brain through elastic registration of histological sections

In this paper we describe a method for the reconstruction and visualization of functional models of monkey brains. Models are built through the registration of high resolution images obtained from the scanning of histological sections with reference photos taken during the brain slicing. From the histological sections it is also possible to acquire specifically activated neuron coordinates introducing functional information in the model. Due to the specific nature of the images (texture information is useless and the sections could be deformed when they were cut and placed on glass) we solved the registration problem by extracting corresponding cerebral cortex borders (extracted with a snake algorithm), and computing from their deformation an image transform modeled as an affine deformation plus a non-linear field evaluated as an elastically constrained deformation minimizing contour distances. Registered images and contours are used then to build 3D models of specific brains by a software tool allowing the interactive visualization of cortical volumes together with the spatially referenced neurons classified and differently colored according to their functionalities

Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching

<p>Abstract</p> <p>Background</p> <p>Accurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI). Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information.</p> <p>Methods</p> <p>First all CT slices are aligned by detecting the ideal midlines in all images. The initial estimation of the ideal midline of the brain is found based on skull symmetry and then the initial estimate is further refined using detected anatomical features. Then a two-step method is used for ventricle segmentation. First a low-level segmentation on each pixel is applied on the CT images. For this step, both Iterated Conditional Mode (ICM) and Maximum A Posteriori Spatial Probability (MASP) are evaluated and compared. The second step applies template matching algorithm to identify objects in the initial low-level segmentation as ventricles. Experiments for ventricle segmentation are conducted using a relatively large CT dataset containing mild and severe TBI cases.</p> <p>Results</p> <p>Experiments show that the acceptable rate of the ideal midline detection is over 95%. Two measurements are defined to evaluate ventricle recognition results. The first measure is a sensitivity-like measure and the second is a false positive-like measure. For the first measurement, the rate is 100% indicating that all ventricles are identified in all slices. The false positives-like measurement is 8.59%. We also point out the similarities and differences between ICM and MASP algorithms through both mathematically relationships and segmentation results on CT images.</p> <p>Conclusion</p> <p>The experiments show the reliability of the proposed algorithms. The novelty of the proposed method lies in its incorporation of anatomical features for ideal midline detection and the two-step ventricle segmentation method. Our method offers the following improvements over existing approaches: accurate detection of the ideal midline and accurate recognition of ventricles using both anatomical features and spatial templates derived from Magnetic Resonance Images.</p

Effect of constitutive inactivation of the myostatin gene on the gain in muscle strength during postnatal growth in two murine models

INTRODUCTION: The effect of constitutive inactivation of the gene encoding myostatin on the gain in muscle performance during postnatal growth has not been well characterized. METHODS: We analyzed 2 murine myostatin knockout (KO) models, (i) the Lee model (KO(Lee) ) and (ii) the Grobet model (KO(Grobet) ), and measured the contraction of tibialis anterior muscle in situ. RESULTS: Absolute maximal isometric force was increased in 6-month-old KO(Lee) and KO(Grobet) mice, as compared to wild-type mice. Similarly, absolute maximal power was increased in 6-month-old KO(Lee) mice. In contrast, specific maximal force (relative maximal force per unit of muscle mass was decreased in all 6-month-old male and female KO mice, except in 6-month-old female KO(Grobet) mice, whereas specific maximal power was reduced only in male KO(Lee) mice. CONCLUSIONS: Genetic inactivation of myostatin increases maximal force and power, but in return it reduces muscle quality, particularly in male mice. Muscle Nerve 55: 254-261, 2017

Model-Based Segmentation

International audienceThis chapter starts with a brief introduction into model-based segmentation, explaining the basic concepts and different approaches. Subsequently, two segmentation approaches are presented in more detail: First, the method of deformable simplex meshes is described, explaining the special properties of the simplex mesh and the formulation of the internal forces. Common choices for image forces are presented, and how to evolve the mesh to adapt to certain structures. Second, the method of point-based statistical shape models (SSMs) is described. The model construction process is explained and the point correspondence problem is treated in detail. Several approaches of how gray level appearance can be modeled are presented, and search algorithms that use this knowledge to segment the modeled structures in new images are described