A novel method to compute the contact surface area between an organ and cancer tissue
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916142413512704 |
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| author | Bulanti, Alessandra Carfì, Alessandro Traverso, Paolo Terrone, Carlo Mastrogiovanni, Fulvio |
| author_facet | Bulanti, Alessandra Carfì, Alessandro Traverso, Paolo Terrone, Carlo Mastrogiovanni, Fulvio |
| contents | With "contact surface area" (CSA) we refers to the area of contact between a tumor and an organ. This indicator has been identified as a predictive factor for surgical peri-operative parameters, particularly in the context of kidney cancer. However, state-of-the-art algorithms for computing the CSA rely on assumptions about the tumor shape and require manual human annotation. In this study, we introduce an innovative method that relies on 3D reconstructions of tumors and organs to provide an accurate and objective estimate of the CSA. Our approach consists of a segmentation protocol for reconstructing organs and tumors from Computed Tomography (CT) images and an algorithm leveraging the reconstructed meshes to compute the CSA. With the aim to contributing to the literature with replicable results, we provide an open-source implementation of our algorithm, along with an easy-to-use graphical user interface to support its adoption and widespread use. We evaluated the accuracy of our method using both a synthetic dataset and reconstructions of 87 real tumor-organ pairs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16857 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A novel method to compute the contact surface area between an organ and cancer tissue Bulanti, Alessandra Carfì, Alessandro Traverso, Paolo Terrone, Carlo Mastrogiovanni, Fulvio Image and Video Processing Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition With "contact surface area" (CSA) we refers to the area of contact between a tumor and an organ. This indicator has been identified as a predictive factor for surgical peri-operative parameters, particularly in the context of kidney cancer. However, state-of-the-art algorithms for computing the CSA rely on assumptions about the tumor shape and require manual human annotation. In this study, we introduce an innovative method that relies on 3D reconstructions of tumors and organs to provide an accurate and objective estimate of the CSA. Our approach consists of a segmentation protocol for reconstructing organs and tumors from Computed Tomography (CT) images and an algorithm leveraging the reconstructed meshes to compute the CSA. With the aim to contributing to the literature with replicable results, we provide an open-source implementation of our algorithm, along with an easy-to-use graphical user interface to support its adoption and widespread use. We evaluated the accuracy of our method using both a synthetic dataset and reconstructions of 87 real tumor-organ pairs. |
| title | A novel method to compute the contact surface area between an organ and cancer tissue |
| topic | Image and Video Processing Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.16857 |