Articles by Rajesh Kumar in JoVE
Breast Milk Enhances Growth of Enteroids: An Ex Vivo Model of Cell Proliferation Wyatt E. Lanik1, Lily Xu2, Cliff J. Luke1, Elise Z. Hu2, Pranjal Agrawal2, Victoria S. Liu2, Rajesh Kumar1, Alexa M. Bolock1, Congrong Ma3, Misty Good1 1Division of Newborn Medicine, Department of Pediatrics, Washington University School of Medicine, 2Washington University, 3Division of Newborn Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine This protocol describes how to establish an enteroid culture system from neonatal mouse or premature human intestine as well as an efficient method to collect milk from mice.
Other articles by Rajesh Kumar on PubMed
A General Framework for Wireless Capsule Endoscopy Study Synopsis Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society. | Pubmed ID: 24974010 We present a general framework for analysis of wireless capsule endoscopy (CE) studies. The current available workstations provide a time-consuming and labor-intense work-flow for clinicians which requires the inspection of the full-length video. The development of a computer-aided diagnosis (CAD) CE workstation will have a great potential to reduce the diagnostic time and improve the accuracy of assessment. We propose a general framework based on hidden Markov models (HMMs) for study synopsis that forms the computational engine of our CAD workstation. Color, edge and texture features are first extracted and analyzed by a Support Vector Machine classifier, and then encoded as the observations for the HMM, uniquely combining the temporal information during the assessment. Experiments were performed on 13 full-length CE studies, instead of selected images previously reported. The results (e.g. 0.933 accuracy with 0.933 recall for detection of polyps) show that our framework achieved promising performance for multiple classification. We also report the patient-level CAD assessment of complete CE studies for multiple abnormalities, and the patient-level validation demonstrates the effectiveness and robustness of our methods.