Background: The liver is one of the most common sites for the spread of pancreatic ductal adenocarcinoma (PDAC) cells, with metastases present in about 80% of patients. Clinical and preclinical studies of PDAC require quantification of the liver’s metastatic burden from several acquired images, which can benefit from automatic image segmentation tools. Methods: We developed three neural networks based on U-net architecture to automatically segment the healthy liver area (HL), the metastatic liver area (MLA), and liver metastases (LM) in micro-CT images of a mouse model of PDAC with liver metastasis. Three alternative U-nets were trained for each structure to be segmented following appropriate image preprocessing and the one with the highest performance was then chosen and applied for each case. Results: Good performance was achieved, with accuracy of 92.6%, 88.6%, and 91.5%, specificity of 95.5%, 93.8%, and 99.9%, Dice of 71.6%, 74.4%, and 29.9%, and negative predicted value (NPV) of 97.9%, 91.5%, and 91.5% on the pilot validation set for the chosen HL, MLA, and LM networks, respectively. Conclusions: The networks provided good performance and advantages in terms of saving time and ensuring reproducibility.

(2024). Automatic Segmentation of Metastatic Livers by Means of U-Net-Based Procedures [journal article - articolo]. In CANCERS. Retrieved from https://hdl.handle.net/10446/290265

Automatic Segmentation of Metastatic Livers by Means of U-Net-Based Procedures

Tiraboschi, Camilla;Parenti, Federica;Lanzarone, Ettore
2024-01-01

Abstract

Background: The liver is one of the most common sites for the spread of pancreatic ductal adenocarcinoma (PDAC) cells, with metastases present in about 80% of patients. Clinical and preclinical studies of PDAC require quantification of the liver’s metastatic burden from several acquired images, which can benefit from automatic image segmentation tools. Methods: We developed three neural networks based on U-net architecture to automatically segment the healthy liver area (HL), the metastatic liver area (MLA), and liver metastases (LM) in micro-CT images of a mouse model of PDAC with liver metastasis. Three alternative U-nets were trained for each structure to be segmented following appropriate image preprocessing and the one with the highest performance was then chosen and applied for each case. Results: Good performance was achieved, with accuracy of 92.6%, 88.6%, and 91.5%, specificity of 95.5%, 93.8%, and 99.9%, Dice of 71.6%, 74.4%, and 29.9%, and negative predicted value (NPV) of 97.9%, 91.5%, and 91.5% on the pilot validation set for the chosen HL, MLA, and LM networks, respectively. Conclusions: The networks provided good performance and advantages in terms of saving time and ensuring reproducibility.
articolo
2024
Tiraboschi, Camilla; Parenti, Federica; Sangalli, Fabio; Resovi, Andrea; Belotti, Dorina; Lanzarone, Ettore
(2024). Automatic Segmentation of Metastatic Livers by Means of U-Net-Based Procedures [journal article - articolo]. In CANCERS. Retrieved from https://hdl.handle.net/10446/290265
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/290265
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