A Multiple Geometric Deformable Model Framework for Homeomorphic 3D Medical Image Segmentation

Identification of spatially variable genes with graph cuts

Single-cell gene expression data with positional information is critical to dissect mechanisms and architectures of multicellular organisms, but the potential is limited by the scalability of current data analysis strategies. Here, we present scGCO, a method based on fast optimization of hidden Markov Random Fields with graph cuts to identify spatially variable genes. Comparing to existing methods, scGCO delivers a superior performance with lower false positive rate and improved specificity, while demonstrates a more robust performance in the presence of noises. Critically, scGCO scales near linearly with inputs and demonstrates orders of magnitude better running time and memory requirement than existing methods, and could represent a valuable solution when spatial transcriptomics data grows into millions of data points and beyond.

Single-cell gene expression data with positional information is critical to dissect mechanisms and architectures of multicellular organisms, but the potential is limited by the scalability of current data analysis strategies. Here the authors develop a highly scalable method, scGCO, to identify genes whose expression values form spatial patterns from spatial transcriptomics data.

Three-dimensional measurement of small inner surface profiles using feature-based 3-D panoramic registration

Rapid development in the performance of sophisticated optical components, digital image sensors, and computer abilities along with decreasing costs has enabled three-dimensional (3-D) optical measurement to replace more traditional methods in manufacturing and quality control. The advantages of 3-D optical measurement, such as noncontact, high accuracy, rapid operation, and the ability for automation, are extremely valuable for inline manufacturing. However, most of the current optical approaches are eligible for exterior instead of internal surfaces of machined parts. A 3-D optical measurement approach is proposed based on machine vision for the 3-D profile measurement of tiny complex internal surfaces, such as internally threaded holes. To capture the full topographic extent (peak to valley) of threads, a side-view commercial rigid scope is used to collect images at known camera positions and orientations. A 3-D point cloud is generated with multiview stereo vision using linear motion of the test piece, which is repeated by a rotation to form additional point clouds. Registration of these point clouds into a complete reconstruction uses a proposed automated feature-based 3-D registration algorithm. The resulting 3-D reconstruction is compared with x-ray computed tomography to validate the feasibility of our proposed method for future robotically driven industrial 3-D inspection.

Parcellation of the Thalamus Using Diffusion Tensor Images and a Multi-object Geometric Deformable Model

The thalamus is a sub-cortical gray matter structure that relays signals between the cerebral cortex and midbrain. It can be parcellated into the thalamic nuclei which project to different cortical regions. The ability to automatically parcellate the thalamic nuclei could lead to enhanced diagnosis or prognosis in patients with some brain disease. Previous works have used diffusion tensor images (DTI) to parcellate the thalamus, using either tensor similarity or cortical connectivity as information driving the parcellation. In this paper, we propose a method that uses the diffusion tensors in a different way than previous works to guide a multiple object geometric deformable model (MGDM) for parcellation. The primary eigenvector (PEV) is used to indicate the homogeneity of fiber orientations. To remove the ambiguity due to the fact that the PEV is an orientation, we map the PEV into a 5D space known as the Knutsson space. An edge map is then generated from the 5D vector to show divisions between regions of aligned PEV's. The generalized gradient vector flow (GGVF) calculated from the edge map drives the evolution of the boundary of each nucleus. Region based force, balloon force, and curvature force are also employed to refine the boundaries. Experiments have been carried out on five real subjects. Quantitative measures show that the automated parcellation agrees with the manual delineation of an expert under a published protocol.

A Multiple Object Geometric Deformable Model for Image Segmentation

Deformable models are widely used for image segmentation, most commonly to find single objects within an image. Although several methods have been proposed to segment multiple objects using deformable models, substantial limitations in their utility remain. This paper presents a multiple object segmentation method using a novel and efficient object representation for both two and three dimensions. The new framework guarantees object relationships and topology, prevents overlaps and gaps, enables boundary-specific speeds, and has a computationally efficient evolution scheme that is largely independent of the number of objects. Maintaining object relationships and straightforward use of object-specific and boundary-specific smoothing and advection forces enables the segmentation of objects with multiple compartments, a critical capability in the parcellation of organs in medical imaging. Comparing the new framework with previous approaches shows its superior performance and scalability.

A Novel Contrast for DTI Visualization for Thalamus Delineation

It has been recently shown that thalamic nuclei can be automatically segmented using diffusion tensor images (DTI) under the assumption that principal fiber orientation is similar within a given nucleus and distinct between adjacent nuclei. Validation of these methods, however, is challenging because manual delineation is hard to carry out due to the lack of images showing contrast between the nuclei. In this paper, we present a novel gray-scale contrast for DTI visualization that accentuates voxels in which the orientations of the principal eigenvectors are changing, thus providing an edge map for the delineation of some thalamic nuclei. The method uses the principal fiber orientation computed from the diffusion tensors computed at each voxel. The three-dimensional orientations of the principal eigenvectors are represented as five dimensional vectors and the spatial gradient (matrix) of these vectors provide information about spatial changes in tensor orientation. In particular, an edge map is created by computing the Frobenius norm of this gradient matrix. We show that this process reveals distinct edges between large nuclei in the thalamus, thereby making manual delineation of the thalamic nuclei possible. We briefly describe a protocol for the manual delineation of thalamic nuclei based on this edge map used in conjunction with a registered T1-weighted MR image, and present a preliminary multi-rater evaluation of the volumes of thalamic nuclei in several subjects.