Fibroblast growth factor 4 and its novel splice isoform have opposing effects on the maintenance of human embryonic stem cell self-renewal.

Human embryonic stem cells (HESCs) are unique in their capacity to self-renew while remaining pluripotent. This undifferentiated state must be actively maintained by secreted factors. To identify autocrine factors that may support HESC growth, we have taken a global genetic approach. Microarray analysis identified fibroblast growth factor 4 (FGF4) as a prime candidate for autocrine signaling. Furthermore, the addition of recombinant FGF4 to HESCs supports their proliferation. We show that FGF4 is produced by multiple undifferentiated HESC lines, along with a novel fibroblast growth factor 4 splice isoform (FGF4si) that codes for the amino-terminal half of FGF4. Strikingly, although FGF4 supports the undifferentiated growth of HESCs, FGF4si effectively counters its effect. Furthermore, we show that FGF4si is an antagonist of FGF4, shutting down FGF4-induced Erk1/2 phosphorylation. Expression analysis shows that both isoforms are expressed in HESCs and early differentiated cells. However, whereas FGF4 ceases to be expressed in mature differentiated cells, FGF4si continues to be expressed after cell differentiation. Targeted knockdown of FGF4 using small interfering RNA increased differentiation of HESCs, demonstrating the importance of endogenous FGF4 signaling in maintaining their pluripotency. Taken together, these results suggest a growth-promoting role for FGF4 in HESCs and a putative feedback inhibition mechanism by a novel FGF4 splice isoform that may serve to promote differentiation at later stages of development.

A geometric method for the detection and correction of segmentation leaks of anatomical structures in volumetric medical images.

PURPOSE: Patient-specific models of anatomical structures and pathologies generated from volumetric medical images play an increasingly central role in many aspects of patient care. A key task in generating these models is the segmentation of anatomical structures and pathologies of interest. Although numerous segmentation methods are available, they often produce erroneous delineations that require time-consuming modifications. METHODS: We present a new geometry-based algorithm for the reliable detection and correction of segmentation errors in volumetric medical images. The method is applicable to anatomical structures consisting of a few 3D star-shaped components. First, it detects segmentation errors by casting rays from the initial segmentation interior to its outer surface. It then classifies the segmentation surface into correct and erroneous regions by minimizing an energy functional that incorporates first- and second-order properties of the rays lengths. Finally, it corrects the segmentation errors by computing new locations for the erroneous surface points by Laplace deformation so that the new surface has maximum smoothness with respect to the rays-length gradient magnitude. RESULTS: Our evaluation on initial segmentations of 16 abdominal aortic aneurysm and 12 lung tumors in CT scans obtained by both adaptive region-growing and active contours level-set segmentation improved the volumetric overlap error by 66 and 70.5% respectively, with respect to the ground-truth. CONCLUSIONS: The advantages of our method are that it is independent of the initial segmentation algorithm that covers a variety of anatomical structures and pathologies, that it does not require a shape prior, and that it requires minimal user interaction.

Reduced-dose patient to baseline CT rigid registration in 3D Radon space.

We present a new method for rigid registration of CT scans in Radon space. The inputs are the two 3D Radon transforms of the CT scans, one densely sampled and the other sparsely sampled. The output s the rigid transformation that best matches them. The algorithm starts by finding the best matching between each direction vector in the sparse transform and the corresponding direction vector in the dense transform. It then solves the system of linear equations derived from the direction vector pairs. Our method can be used to register two CT scans and to register a baseline scan to the patient with reduced-dose scanning without compromising registration accuracy. Our preliminary simulation results on the Shepp-Logan head phantom dataset and a pair of clinical head CT scans indicates that our 3D Radon space rigid registration method performs significantly better than image-based registration for very few scan angles and comparably for densely-sampled scans.

Image segmentation errors correction by mesh segmentation and deformation.

Volumetric image segmentation methods often produce delineations of anatomical structures and pathologies that require user modifications. We present a new method for the correction of segmentation errors. Given an initial geometrical mesh, our method semi automatically identifies the mesh vertices in erroneous regions with min-cut segmentation. It then deforms the mesh by correcting its vertex coordinates with Laplace deformation based on local geometrical properties. The key advantages of our method are that: (1) it supports fast user interaction on a single surface rendered 2D view; (2) its parameters values are fixed to the same value for all cases; (3) it is independent of the initial segmentation method, and; (4) it is applicable to a variety of anatomical structures and pathologies. Experimental evaluation on 44 initial segmentations of kidney and kidney vessels from CT scans show an improvement of 83% and 75% in the average surface distance and the volume overlap error between the initial and the corrected segmentations with respect to the ground-truth.