Eus Pathology with Digital Anatomy Correlation A Text and Atlas 1st edition by Manioop Bhutani, John Doutsch – Ebook PDF Instant Download/Delivery: 1607950286, 978-1607950288
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ISBN 10: 1607950286
ISBN 13: 978-1607950288
Author: Manioop Bhutani, John Doutsch
Atlas of Pathologic lesions by Endoscopic Ultrasonography (EUS) is a dedicated text to learn pathologic images seen during EUS. The digital anatomy correlation used in this work is the natural continuation of efforts to apply the University of Colorado Visible Human data set to gastroenterology. The Visible Human data set was created by Dr. Vic Spitzer and colleagues at the University of Colorado and is currently housed at the university’s Center for Human Simulation. The data set consists of high resolution transaxial digital images captured as cadavers were abraded away at 1 mm or less depths. These images are compiled into blocks of data and each structure is identified. This information can be used to pull out and manipulate 3-D structures as well as allowing one to review planar anatomy in any orientation. Using the Visible Human dataset, one should be able to find a normal anatomy correlate to any image found during a EUS examination. However, as important as normal anatomy is, it is the abnormal features which are the crux of an EUS examination. Endosonographers are asked to define lumps, bumps, cysts to find correlates for symptoms and abnormal laboratory findings. Accuracy requires a tremendous amount of skill and experience. To help in this task, we have assembled chapters from a world-wide group of expert endosonographers. These authors have shared their insight and images to help the readers of this work better see and understand some of the complexities uncovered during a EUS evaluation.
Eus Pathology with Digital Anatomy Correlation A Text and Atlas 1st Table of contents:
Chapter1 : Introduction
Chapter 2: Content
Chapter 3: Conclusion
Chapter 4: Appendices
Chapter 5: Glossary
Chapter 6: References
Chapter 7: Index
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Tags: Manioop Bhutani, John Doutsch, Eus Pathology, Digital Anatomy