Thesis Project Form

Pectus Excavatum (PE) is the most common congenital chest wall deformity, characterized by a posterior displacement of the sternum and adjacent costal cartilages, resulting in a depression of the anterior thoracic wall. The assessment of deformity severity is essential for clinical evaluation and surgical planning, particularly in pediatric patients.

Computed Tomography (CT) provides detailed anatomical information about the thoracic structures and is widely used to quantify chest wall deformities through established clinical indices, such as the Haller Index, Correction Index, Asymmetry Index, and Flatness Index. However, these measurements are generally performed manually on selected axial slices, potentially introducing inter-observer variability and failing to capture the three-dimensional extent of the deformity.

This thesis aims to investigate semi-automatic image processing methods for extracting quantitative morphological descriptors from CT images, extending conventional two-dimensional measurements to a volumetric characterization of Pectus Excavatum.

Specifically, the main stages of the work will consist of:

- Analysis and preprocessing of thoracic CT images, including image normalization, identification of the anatomical region of interest, and evaluation of image quality.

- Development and implementation of image processing techniques for the segmentation of thoracic structures, with particular attention to the identification of the inner and outer chest wall contours.

- Semi-automatic computation of conventional clinical indices, including Haller Index, Correction Index, Asymmetry Index, and Flatness Index, from CT images.

- Development or adaptation of a volumetric analysis approach to quantify the extent of the sternal depression, including the estimation of the Volumetric Correction Index (VCI), based on a simulated correction of the anterior chest wall morphology.

- Quantitative evaluation of the proposed methods through comparison with manually obtained measurements, where available, to investigate accuracy, reproducibility, and potential reduction in processing time.

The ultimate aim of the proposed work is to develop a reproducible image processing pipeline supporting the quantitative assessment of Pectus Excavatum from CT imaging, providing clinicians with complementary morphological information for deformity evaluation and potential surgical planning.

Medical image processing and analysis of clinical CT data. Development and implementation of segmentation and image processing algorithms using suitable programming environments (e.g., Python or MATLAB). Extraction of quantitative morphological and volumetric descriptors from medical images. Three-dimensional reconstruction and visualization of thoracic anatomy. Statistical analysis and comparison of automatically and manually obtained clinical measurements.