The liver, being the largest solid organ in the human body, is one of the most important players in metabolism and digestion processes. It is also a site where both primary and secondary tumors — originating from distant organs such as the lungs or other abdominal parts such as the pancreas and colon — can originate or metastasize. Therefore, the liver is regularly screened for the presence of lesions. These lesions require precise segmentation techniques to accurately diagnose cancer and improve patient monitoring, disease progression, and response to treatment. In this paper, a slightly modified version of a DeepLabV3+ network, a well–known and state–of–the–art segmentation model, paired with a Hidden Markov Model (HMM) based noise reduction module, is employed and trained on the Medical Segmentation Decathlon (MSD) liver tumor data set. This collection of liver lesions is a fraction of the MSD international challenge dedicated to identifying a general– purpose algorithm for medical image segmentation. The model is then evaluated on the test set of the same dataset with pixel–pixel accuracy and Intersection over Union (IoU).

Tanfoni, M., Ceroni, E.G., Maggini, M., Pancino, N., Bianchini, M. (2024). A Hybrid Deep Learning Approach for Liver Tumor Segmentation Using DeepLabV3+ and Hidden Markov Models. In 2024 IEEE International Symposium on Systems Engineering (ISSE) (pp.1-5). New York : IEEE [10.1109/ISSE63315.2024.10741139].

A Hybrid Deep Learning Approach for Liver Tumor Segmentation Using DeepLabV3+ and Hidden Markov Models

Marco Tanfoni;Elia Giuseppe Ceroni;Marco Maggini;Niccolò Pancino;Monica Bianchini
2024-01-01

Abstract

The liver, being the largest solid organ in the human body, is one of the most important players in metabolism and digestion processes. It is also a site where both primary and secondary tumors — originating from distant organs such as the lungs or other abdominal parts such as the pancreas and colon — can originate or metastasize. Therefore, the liver is regularly screened for the presence of lesions. These lesions require precise segmentation techniques to accurately diagnose cancer and improve patient monitoring, disease progression, and response to treatment. In this paper, a slightly modified version of a DeepLabV3+ network, a well–known and state–of–the–art segmentation model, paired with a Hidden Markov Model (HMM) based noise reduction module, is employed and trained on the Medical Segmentation Decathlon (MSD) liver tumor data set. This collection of liver lesions is a fraction of the MSD international challenge dedicated to identifying a general– purpose algorithm for medical image segmentation. The model is then evaluated on the test set of the same dataset with pixel–pixel accuracy and Intersection over Union (IoU).
2024
979-8-3503-5372-3
Tanfoni, M., Ceroni, E.G., Maggini, M., Pancino, N., Bianchini, M. (2024). A Hybrid Deep Learning Approach for Liver Tumor Segmentation Using DeepLabV3+ and Hidden Markov Models. In 2024 IEEE International Symposium on Systems Engineering (ISSE) (pp.1-5). New York : IEEE [10.1109/ISSE63315.2024.10741139].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1280298