Feldman, Michael
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Publication Multiple Oncogenic Viruses Identified in Ocular Surface Squamous Neoplasia in HIV-1 Patients(2010-03-26) Simbiri, Kenneth O; Feldman, Michael; Steenhoff, Andrew P; Bisson, Gregory P; Murakami, Masanao; Robertson, Erle S; Nkomazana, OathokwaBackground Ocular surface squamous neoplasia (OSSN) is a rare cancer that has increased in incidence with the HIV pandemic in Africa. The underlying cause of this cancer in HIV-infected patients from Botswana is not well defined. Results Tissues were obtained from 28 OSSN and 8 pterygia patients. The tissues analyzed from OSSN patients were 83% positive for EBV, 75% were HPV positive, 70% were KSHV positive, 75% were HSV-1/2 positive, and 61% were CMV positive by PCR. Tissues from pterygium patients were 88% positive for EBV, 75% were HPV positive, 50% were KSHV positive, and 60% were CMV positive. None of the patients were JC or BK positive. In situ hybridization and immunohistochemistry analyses further identified HPV, EBV, and KSHV in a subset of the tissue samples. Conclusion We identified the known oncogenic viruses HPV, KSHV, and EBV in OSSN and pterygia tissues. The presence of these tumor viruses in OSSN suggests that they may contribute to the development of this malignancy in the HIV population. Further studies are necessary to characterize the molecular mechanisms associated with viral antigens and their potential role in the development of OSSN.Publication Registering Histological and MR Images of Prostate for Image-based Cancer Detection(2007-11-01) Ou, Yangming; Feldman, Michael; Tomaszewski, John; Davatzikos, Christos; Zhan, Yiqiang; Shen, DinggangRationale and Objectives Needle biopsy is currently the only way to confirm prostate cancer. To increase prostate cancer diagnostic rate, needles are expected to be deployed at suspicious cancer locations. High contrast MR imaging provides a powerful tool for detecting suspicious cancerous tissues. To do this, MR appearances of cancerous tissue should be characterized and learned from a sufficient number of prostate MR images with known cancer information. However, ground-truth cancer information is only available in histological images. Therefore, it is necessary to warp ground-truth cancerous regions in histological images to MR images by a registration procedure. The objective of this paper is to develop a registration technique for aligning histological and MR images of the same prostate. Material and Methods Five pairs of histological and T2-weighted MR images of radical prostatectomy specimens are collected. For each pair, registration is guided by two sets of correspondences that can be reliably established on prostate boundaries and internal salient blob-like structures of histological and MR images. Results Our developed registration method can accurately register histological and MR images. It yields results comparable to manual registration, in terms of landmark distance and volume overlap. It also outperforms both affine registration and boundary-guided registration methods. Conclusions We have developed a novel method for deformable registration of histological and MR images of the same prostate. Besides the collection of ground-truth cancer information in MR images, the method has other potential applications. An automatic, accurate registration of histological and MR images actually builds a bridge between in vivo anatomical information and ex vivo pathological information, which is valuable for various clinical studies.Publication Non-Rigid Registration between Histological and MR Images of the Prostate: A Joint Segmentation and Registration Framework(2009-06-01) Ou, Yangming; Shen, Dinggang; Feldman, Michael; Tomaszewski, John; Davatzikos, ChristosThis paper presents a 3D non-rigid registration algorithm between histological and MR images of the prostate with cancer. To compensate for the loss of 3D integrity in the histology sectioning process, series of 2D histological slices are first reconstructed into a 3D histological volume. After that, the 3D histology-MRI registration is obtained by maximizing a) landmark similarity and b) cancer region overlap between the two images. The former aims to capture distortions at prostate boundary and internal bloblike structures; and the latter aims to capture distortions specifically at cancer regions. In particular, landmark similarities, the former, is maximized by an annealing process, where correspondences between the automatically-detected boundary and internal landmarks are iteratively established in a fuzzy-to-deterministic fashion. Cancer region overlap, the latter, is maximized in a joint cancer segmentation and registration framework, where the two interleaved problems – segmentation and registration – inform each other in an iterative fashion. Registration accuracy is established by comparing against human-rater-defined landmarks and by comparing with other methods. The ultimate goal of this registration is to warp the histologically-defined cancer ground truth into MRI, for more thoroughly understanding MRI signal characteristics of the prostate cancerous tissue, which will promote the MRI-based prostate cancer diagnosis in the future studies.