Development and Evaluation of a Rib Statistical Shape Model for Thoracic Surgery

Antonia A Pontiki, Sara De Angelis, Connor Dibblin, Isabella Trujillo-Cortes, Pablo Lamata, Richard Housden, Giulia Benedetti, Andrea Bille, Kawal Rhode

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

Patients with advanced cancer undergoing chest wall resection may require reconstruction. Currently, rib prostheses are created by segmenting computed tomography images, which is time-consuming and labour intensive. The aim was to optimise the production of digital rib models based on a patient's age, weight, height and gender. A statistical shape model of human ribs was created and used to synthetise rib models, which were compared to the ones produced by segmentation and mirroring. The segmentation took 11.56-1.60 min compared to 0.027 -0.009 using the new technique. The average mesh error between the mirroring technique and segmentation was 0.58-0.25 mm (right ribs), and 0.87-0.18 mm (left ribs), compared to 1.37-0.66 mm ({p} < 0.0001) and 1.68 -0.77 mm ({p} < 0.05), respectively, for the new technique. The new technique is promising for the efficiency and ease-of-use in the clinical environment. Clinical Relevance - This is an optimised 3D modelling method providing clinicians with a time-efficient technique to create patient-specific rib prostheses, without any expertise or software knowledge required.

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