Background Using practical examples, this report aims to highlight the clinical value of patient-specific three-dimensional (3D) models, obtained segmenting multidetector computed tomography (MDCT) images, for preoperative planning in general surgery. Methods In this study, segmentation and 3D model generation were performed using a semiautomatic tool developed in the authors’ laboratory. Their segmentation procedure is based on the neighborhood connected regiongrowing algorithm that, appropriately parameterized for the anatomy of interest and combined with the optimal segmentation sequence, generates good-quality 3D images coupled with facility of use. Using a touch screen monitor, manual refining can be added to segment structures unsuitable for automatic reconstruction. Three-dimensional models of 10 candidates for major general surgery procedures were presented to the operating surgeons for evaluation. A questionnaire then was administered after surgery to assess the perceived added value of the new technology. Results The questionnaire results were very positive. The authors recorded the diffuse opinion that planning the procedure using a segmented data set allows the surgeon to plan critical interventions with better awareness of the specific patient anatomy and consequently facilitates choosing the best surgical approach. Conclusions The benefit shown in this report supports a wider use of segmentation software in clinical practice, even taking into account the extra time and effort required to learn and use these systems.
Value of multidetector computed tomography image segmentation for preoperative planning in general surgery
FERRARI, VINCENZO;CARBONE, MARINA;MELFI, FRANCA;FERRARI, MAURO;
2012-01-01
Abstract
Background Using practical examples, this report aims to highlight the clinical value of patient-specific three-dimensional (3D) models, obtained segmenting multidetector computed tomography (MDCT) images, for preoperative planning in general surgery. Methods In this study, segmentation and 3D model generation were performed using a semiautomatic tool developed in the authors’ laboratory. Their segmentation procedure is based on the neighborhood connected regiongrowing algorithm that, appropriately parameterized for the anatomy of interest and combined with the optimal segmentation sequence, generates good-quality 3D images coupled with facility of use. Using a touch screen monitor, manual refining can be added to segment structures unsuitable for automatic reconstruction. Three-dimensional models of 10 candidates for major general surgery procedures were presented to the operating surgeons for evaluation. A questionnaire then was administered after surgery to assess the perceived added value of the new technology. Results The questionnaire results were very positive. The authors recorded the diffuse opinion that planning the procedure using a segmented data set allows the surgeon to plan critical interventions with better awareness of the specific patient anatomy and consequently facilitates choosing the best surgical approach. Conclusions The benefit shown in this report supports a wider use of segmentation software in clinical practice, even taking into account the extra time and effort required to learn and use these systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
