Erhan Bas

Thalamocortical input onto layer 5 pyramidal neurons measured using quantitative large-scale array tomography

The subcellular locations of synapses on pyramidal neurons strongly influences dendritic integration and synaptic plasticity. Despite this, there is little quantitative data on spatial distributions of specific types of synaptic input. Here we use array tomography (AT), a high-resolution optical microscopy method, to examine thalamocortical (TC) input onto layer 5 pyramidal neurons. We first verified the ability of AT to identify synapses using parallel electron microscopic analysis of TC synapses in layer 4. We then use large-scale array tomography (LSAT) to measure TC synapse distribution on L5 pyramidal neurons in a 1.00 × 0.83 × 0.21 mm3 volume of mouse somatosensory cortex. We found that TC synapses primarily target basal dendrites in layer 5, but also make a considerable input to proximal apical dendrites in L4, consistent with previous work. Our analysis further suggests that TC inputs are biased toward certain branches and, within branches, synapses show significant clustering with an excess of TC synapse nearest neighbors within 5–15 μm compared to a random distribution. Thus, we show that AT is a sensitive and quantitative method to map specific types of synaptic input on the dendrites of entire neurons. We anticipate that this technique will be of wide utility for mapping functionally-relevant anatomical connectivity in neural circuits.

Measurement of brain iron distribution in Hallevorden-Spatz syndrome

To investigate spatial distribution of iron accumulation in the globus pallidus (GP) in patients with Hallevorden‐Spatz syndrome (HSS) using phase imaging. We compared sensitivity of a phase imaging technique to relaxation rate measurement methods (R1,R2,R2*) for iron quantification.

Biological reactivity of nanoparticles: mosaics from optical microscopy videos of giant lipid vesicles

Emerging fields such as nanomedicine and nanotoxicology, demand new information on the effects of nanoparticles on biological membranes and lipid vesicles are suitable as an experimental model for bio-nano interaction studies. This paper describes image processing algorithms which stitch video sequences into mosaics and recording the shapes of thousands of lipid vesicles, which were used to assess the effect of CoFe(2)O(4) nanoparticles on the population of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine lipid vesicles. The applicability of this methodology for assessing the potential of engineered nanoparticles to affect morphological properties of lipid membranes is discussed.

Principal Curved Based Retinal Vessel Segmentation towards Diagnosis of Retinal Diseases

The extraction of retinal vessels plays an important role in the diagnosis and study of retinal diseases, such as Age-related Macular Degeneration (AMD), Diabetic Retinopathy, Retinopathy of Prematurity (ROP). Vessel diameters, tortuosity, branch lengths, angles, and bifurcations are essential to diagnosing these diseases. However, this is a challenging task due to high noise levels, the low contrast of thin vessels to the background, non-uniform illumination, and the central light reflex. Our goal here is to develop a framework to accurately segment the retinal vessels as a preprocessing step for the feature extraction of the vessels towards the future disease diagnosis. In this paper, we present a principal curve based retinal vessel segmentation approach to achieve this goal. We first use the isotropic Gaussian kernel Frangi filter to enhance the retinal vessels and measure the diameters of them. A multiscale principal curve projection and tracing algorithm is then proposed to identify the centerlines of the vessels in the output image of the Franfi filter using the underlying kernel smoothing interpolation of the intensities. The estimated vessel radius from the Frangi filter are used as the bandwidth of the kernel interpolation in the principal curve projection and tracing step. The vessel features toward diagnosing and analyzing the diseases can be extracted from our segmentation results. The presented approach is implemented on a publicly available DRIVE database [16].

Piecewise linear cylinder models for 3-dimensional axon segmentation in Brainbow imagery

Generalized cylinder shapes are ubiquitous in biological systems and image processing techniques to identifies these tubular objects in 3D from biomedical imagery in various modalities is a general problem of interest. One such structure that exhibits branching tubular forms are neuronal networks; specifically, recent developments in microscopy imaging technology allow researchers to acquire massive amounts of 3D color images of neural structures that need to be tracked in 3D to extract structure for the purpose of studying function. In this paper, we propose a piecewise linear generalized cylinder tracing algorithm that exploits both edge and color information in order to automatically trace axons of neurons in Brainbow imagery. Results indicate that the proposed method can successfully trace multiple axons in dense neighborhoods.

Contour-based shape representation using principal curves

Extraction and representation of contours are challenging problems and are crucial for many image processing applications. In this study, given a membership function that returns the score of a point belonging to a contour, we propose a method for contour representation based on the principal curve (PC) of this function. The proposed method provides a piecewise linear representation of the contour with fewer points while preserving shape. Varied experiments are conducted, including lung boundary representation in CT images and shape representation in handwritten images. The results show that the technique provides accurate shape representation.

Local tracing of curvilinear structures in volumetric color images: Application to the Brainbow analysis

In this study, we compare two vectorial tracing methods for 3D color images: (i) a conventional piecewise linear generalized cylinder algorithm that uses color and edge information and (ii) a principal curve tracing algorithm that uses the gradient and Hessian of a given density estimate. We tested the algorithms on synthetic and Brainbow dataset to show the effectiveness of the proposed algorithms. Results indicate that the proposed methods can successfully trace multiple axons in dense neighborhoods.

Sampling on locally defined principal manifolds

We start with a locally defined principal curve definition for a given probability density function (pdf) and define a pairwise manifold score based on local derivatives of the pdf. Proposed manifold score can be used to check if data pairs lie on the same manifold. We use this score to i) cluster nonlinear manifolds having irregular shapes, and ii) (down)sample a selected principal curve with sufficient accuracy sparsely. Our goal is to provide a heuristic-free formulation for principal graph generation and curve parametrization in order to form a basis for a principled principal manifold unwrapping method.

Centerline extraction with principal curve tracing to improve 3D level set esophagus segmentation in CT images

For radiotherapy planning, contouring of target volume and healthy structures at risk in CT volumes is essential. To automate this process, one of the available segmentation techniques can be used for many thoracic organs except the esophagus, which is very hard to segment due to low contrast. In this work we propose to initialize our previously introduced model based 3D level set esophagus segmentation method with a principal curve tracing (PCT) algorithm, which we adapted to solve the esophagus centerline detection problem. To address challenges due to low intensity contrast, we enhanced the PCT algorithm by learning spatial and intensity priors from a small set of annotated CT volumes. To locate the esophageal wall, the model based 3D level set algorithm including a shape model that represents the variance of esophagus wall around the estimated centerline is utilized. Our results show improvement in esophagus segmentation when initialized by PCT compared to our previous work, where an ad hoc centerline initialization was performed. Unlike previous approaches, this work does not need a very large set of annotated training images and has similar performance.

Towards respiration management in radiation treatment of lung tumors: Transferring regions of interest from planning CT to kilovoltage X-ray images

Tracking of lung tumors is imperative for improved radiotherapy treatment. However, the motion of the thoracic organs makes it a complicated task. 4D CT images acquired prior to treatment provide valuable information regarding the motion of organs and tumor, since it is manually annotated. In order to track tumors using treatment-day X-ray images (kV images), we need to find the correspondence with CT images so that projection of tumor region of interest will provide a good estimate about the position of the tumor on the X-ray image. In this study, we propose a method to estimate the alignment and respiration phase corresponding to X-ray images using 4D CT data. Our approach generates Digitally Reconstructed Radiographs (DRRs) using bilateral filter smoothing and computes rigid registration with kV images since the position and orientation of patient might differ between CT and treatment-day image acquisition processes. Instead of using landmark points, our registration method makes use of Kernel Density Estimation over the edges that are not affected much by respiration. To estimate the phase of X-ray, we apply template matching techniques between the lung regions of X-ray and registered DRRs. Our approach gives accurate results for rigid registration and provides a starting point to track tumors using the X-ray images during the treatment.