BackgroundMD students learn anatomy principally studying atlas of 2D images based on the most common anatomical presentations.1 When they start their clinical activity, they have to face with individual anatomical variability and translate their knowledge into a 3D space. Recent developments in computer software and improvements in medical imaging quality have allowed to perform a 3D reconstructions starting from the patient-specific cross-sectional imaging, like CT scan or MRI, through a process called "image segmentation". The advantages of preoperative planning through 3D patient-specific anatomy visualization are well-known and have been elongated for many years.2 However, segmentation is not a widespread skill among doctors so that 3D reconstructions are rarely employed in routine clinical settings. Probably, it is due to consistent initial difficulties with the learning phase of segmentation. PurposeThe aim of our research was to analyze the learning curve to evaluate whether segmentation could easily realized, therefore enhancing and motivating the learning experience. MethodsWe enrolled 12 medical students, who passed Anatomy exam and attended a 4 hour course to learn segmentation procedures. An open-source software for Mac OS X, 3D Slicer, was used to analyze images.3 All participants used the same notebook, MacBook Air 13.3". Starting from e-learning resources, we defined a simple work-flow to segment the following organs: bones, aorta, liver, spleen and both kidneys. We estimated the time employed to segment an abdominal-pelvic CT scan, focusing on selected organs. ResultsAll students completed the abdominal 3D reconstruction with a mean time of 24 min (range: 16-36 min). All times employed by students are reported in Table 1. See Figure 1 as an example of the complete procedure. ConclusionsAccording to our experience, we retain that segmentation is an easy-teachable skill, which should be part of the third millenium surgeon's armamentarium. We suggest MD degree curriculum should include this task which requires a minimum investment of resources.
Learning Curve in Segmentation for 3D Reconstructions / G. Sampogna, F. Rizzetto, F. Cigognini, N. Cassina, M. Vertemati, M. Elli. ((Intervento presentato al 1. convegno World Summit on Competency-Based Education tenutosi a Barcellona nel 2016.
|Titolo:||Learning Curve in Segmentation for 3D Reconstructions|
SAMPOGNA, GIANLUCA (Primo)
VERTEMATI, MAURIZIO (Penultimo)
ELLI, MARCO (Ultimo)
|Data di pubblicazione:||ago-2016|
|Parole Chiave:||3D Reconstruction, Anatomy, Didactics|
|Settore Scientifico Disciplinare:||Settore BIO/16 - Anatomia Umana|
|Citazione:||Learning Curve in Segmentation for 3D Reconstructions / G. Sampogna, F. Rizzetto, F. Cigognini, N. Cassina, M. Vertemati, M. Elli. ((Intervento presentato al 1. convegno World Summit on Competency-Based Education tenutosi a Barcellona nel 2016.|
|Appare nelle tipologie:||14 - Intervento a convegno non pubblicato|