Background: In clinical assessment of Pectus Excavatum (PE), the indication to surgery is based not only on symptoms but also on quantitative markers calculated from Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scans. According to clinical routine, these indexes are measured manually by radiologists with limited computer support. This process is time consuming and potentially subjected to inaccuracy and individual variability in measurements. Moreover, the existing indexes have limitations, since they are based on linear measurements performed on single slices rather than on volumetric data derived from all the thoracic scans. Results: In this paper we present an image processing pipeline aimed at providing radiologists with a computer-aid tool in support of diagnosis of PE patients developed in MATLAB® and conceived for MRI images. This framework has a dual purpose: (i) to automatize computation of clinical indexes with a view to ease and standardize pre-operative evaluation; (ii) to propose a new marker of pathological severity based on volumetric analysis and overcoming the limitations of existing axial slice-based indexes. Final designed framework is semi-automatic, requiring some user interventions at crucial steps: this is realized through a Graphical User Interface (GUI) that simplifies the interaction between the user and the tools. We tested our pipeline on 50 pediatric patients from Gaslini Children’s Hospital and performed manual computation of indexes, comparing the results between the proposed tool and gold-standard clinical practice. Automatic indexes provided by our algorithm have shown good agreement with manual measurements by two independent readers. Moreover, the new proposed Volumetric Correction Index (VCI) has exhibited good correlation with standardized markers of pathological severity, proving to be a potential innovative tool for diagnosis, treatment, and follow-up. Conclusions: Our pipeline represents an innovative image processing in PE evaluation, based on MRI images (radiation-free) and providing the clinician with a quick and accurate tool for automatically calculating the classical PE severity indexes and a new more comprehensive marker: the Volumetric Correction Index.

A new tool for assessing Pectus Excavatum by a semi-automatic image processing pipeline calculating the classical severity indexes and a new marker: the Volumetric Correction Index

Tro' R.;Stagnaro N.;Sambuceti V.;Fato M. M.
2022

Abstract

Background: In clinical assessment of Pectus Excavatum (PE), the indication to surgery is based not only on symptoms but also on quantitative markers calculated from Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scans. According to clinical routine, these indexes are measured manually by radiologists with limited computer support. This process is time consuming and potentially subjected to inaccuracy and individual variability in measurements. Moreover, the existing indexes have limitations, since they are based on linear measurements performed on single slices rather than on volumetric data derived from all the thoracic scans. Results: In this paper we present an image processing pipeline aimed at providing radiologists with a computer-aid tool in support of diagnosis of PE patients developed in MATLAB® and conceived for MRI images. This framework has a dual purpose: (i) to automatize computation of clinical indexes with a view to ease and standardize pre-operative evaluation; (ii) to propose a new marker of pathological severity based on volumetric analysis and overcoming the limitations of existing axial slice-based indexes. Final designed framework is semi-automatic, requiring some user interventions at crucial steps: this is realized through a Graphical User Interface (GUI) that simplifies the interaction between the user and the tools. We tested our pipeline on 50 pediatric patients from Gaslini Children’s Hospital and performed manual computation of indexes, comparing the results between the proposed tool and gold-standard clinical practice. Automatic indexes provided by our algorithm have shown good agreement with manual measurements by two independent readers. Moreover, the new proposed Volumetric Correction Index (VCI) has exhibited good correlation with standardized markers of pathological severity, proving to be a potential innovative tool for diagnosis, treatment, and follow-up. Conclusions: Our pipeline represents an innovative image processing in PE evaluation, based on MRI images (radiation-free) and providing the clinician with a quick and accurate tool for automatically calculating the classical PE severity indexes and a new more comprehensive marker: the Volumetric Correction Index.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1075102
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