Development of automatized new indices for radiological assessment of chest-wall deformity and its quantitative evaluation

Abstract

Pre-operative diagnosis of chest-wall deformity is important for successful surgical correction and post-operative evaluation of funnel chest patients. However, conventional indices that define the severity of deformity have several limitations; manually calculated and cannot supply information about asymmetry. We developed four indices that can represent both the depression and the asymmetry of the chest-wall, and can automatically be extracted by computerized image processing technique. Three indices, including eccentricity index (EI), flatness index (FI), and circularity index (CI), were suggested to represent the depression of the chest-wall, and one index, rotation index (RI), to represent the asymmetry of the chest-wall. To verify the feasibility of new indices, several synthetic images and real CT images were used to analyze the performance of new indices and the statistical relationship with conventional Haller index. The experimental results showed possible application of suggested indices to the diagnosis of funnel chest patient. Suggested indices showed clear trends of change with the severity of chest-wall deformation in regards to both the depression and the asymmetry. Results of statistical analysis showed high correlation between new indices and HI, showing possibility of replacing HI.

Radiologic factors related to double-bar insertion in minimal invasive repair of pectus excavatum

“…[6] Since the development of the Haller index, a variety of other indices has been designed; however, the roles of these conventional indices are limited to determining whether or not to perform surgical repair. [7][8][9][10][11][12] In utilizing a minimally invasive procedure for the repair of pectus excavatum, surgeons determine the number of pectus bars required for surgery. Although surgical methods or surgeon's preference may affect the use of double-bar insertion, the morphological characteristics of patients with pectus excavatum are also one of the factors that affect the surgeons' decisions to perform double-bar correction rather than single one.…”

Correlation Between Sternal Depression and Cardiac Rotation in Pectus Excavatum: Evaluation With Helical CT

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with helical CT or on the relations between the degree of sternal depression and cardiac shape and function [5][6][7][8][9][10]. The purpose of this study was to investigate the chest wall deformity and acquired cardiopulmonary changes found with helical CT, especially the effects of sternal depression on cardiac shape and location, in 63 patients who consecutively underwent surgical correction of pectus excavatum.

Materials and Methods Patients

We collected preoperative helical CT data on 63 patients (53 boys, 10 girls; mean age, 9.7 ± 7 years; range 6 months-22 years) with pectus excavatum who consecutively underwent surgical correction during the 3-year period 2006 through 2008.

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Fully automatic initialization method for quantitative assessment of chest-wall deformity in funnel chest patients

“…In previous study [8], we verified statistical correlation between new indices and HI, however, accuracy of new indices themselves were not verified. In further study, we have a plan to compare accuracy and reproducibility of new indices and HI quantitatively, and to extract threshold values of each index that can be used as a diagnostic guideline in clinical field.…”

“…In order to solve this problem, our group suggested a computerized algorithm that extracts the degree of chestwall deformity from a CT image using image processing techniques such as image segmentation and curve fitting, and also recommended four new indices that can be calculated using the suggested algorithm-eccentricity index (EI), flatness index (FI), circularity index (CI), and rotation index (RI) [8]. The proposed method demonstrated enhanced precision and processing time compared with manual calculation, yet it was not a fully automatic system; that is, to extract chest-wall boundary information from the CT image, initial values for image segmentation needed to be manually marked by an operator.…”