Real-Time Liver Lesion Segmentation in Ultrasound Imaging using Deep Learning
Ultrasound (US) imaging is a widely used, non-invasive method for detecting liver tumors and assessing parenchymal changes. However, the inherent variability and noise in US images pose challenges for accurate lesion identification. This study aims to develop and evaluate a deep learning (DL) model...
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| Format: | Article |
| Language: | English |
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Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca
2025-05-01
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| Series: | Applied Medical Informatics |
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| Online Access: | https://ami.info.umfcluj.ro/index.php/AMI/article/view/1194 |
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| author | Marinela-Cristiana URHUŢ Larisa Daniela SĂNDULESCU Costin Teodor STREBA Mădălin MĂMULEANU Adriana CIOCÂLTEU Sergiu Marian CAZACU Suzana DĂNOIU |
| author_facet | Marinela-Cristiana URHUŢ Larisa Daniela SĂNDULESCU Costin Teodor STREBA Mădălin MĂMULEANU Adriana CIOCÂLTEU Sergiu Marian CAZACU Suzana DĂNOIU |
| author_sort | Marinela-Cristiana URHUŢ |
| collection | DOAJ |
| description |
Ultrasound (US) imaging is a widely used, non-invasive method for detecting liver tumors and assessing parenchymal changes. However, the inherent variability and noise in US images pose challenges for accurate lesion identification. This study aims to develop and evaluate a deep learning (DL) model capable of performing real-time segmentation of liver lesions in US scans. A dataset of 50 video examinations was used, from which frames were extracted and manually annotated by an experienced gastroenterologist. The segmentation process was conducted using a U-Net architecture with focal Tversky loss (FTL) to address class imbalance. Two versions of the model were trained with different FTL parameters: Model 1 (α = β = 0.5, γ = 1) and Model 2 (α = 0.7, β = 0.3, γ = 0.75). The models were assessed based on key performance metrics, including intersection over union (IoU), recall, and precision. Model 1 achieved a higher IoU score (0.84) than Model 2. Both models demonstrated inference times between 30 and 80 milliseconds, confirming their feasibility for real-time US applications. Visual analysis showed that Model 1 produced more precise and contiguous lesion segmentation, whereas Model 2 tended to separate lesions that were close together. These findings suggest that the proposed DL models are effective in real-time liver lesion segmentation in US imaging. Model 1, which utilized balanced FTL parameters, demonstrated superior segmentation accuracy.
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| format | Article |
| id | doaj-art-5c41e7f90cec4fcfb98e61ba5ba76532 |
| institution | DOAJ |
| issn | 2067-7855 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca |
| record_format | Article |
| series | Applied Medical Informatics |
| spelling | doaj-art-5c41e7f90cec4fcfb98e61ba5ba765322025-08-20T02:39:51ZengIuliu Hatieganu University of Medicine and Pharmacy, Cluj-NapocaApplied Medical Informatics2067-78552025-05-0147Suppl. 1Real-Time Liver Lesion Segmentation in Ultrasound Imaging using Deep LearningMarinela-Cristiana URHUŢ0Larisa Daniela SĂNDULESCU1Costin Teodor STREBA2Mădălin MĂMULEANU3Adriana CIOCÂLTEU4Sergiu Marian CAZACU5Suzana DĂNOIU6Doctoral School, University of Medicine and Pharmacy of Craiova, Petru Rareş, no. 2, 200349 Craiova, RomaniaDepartment of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Petru Rareş, no. 2, 200349 Craiova, RomaniaUniversity of Medicine and Pharmacy of CraiovaDepartment of Automatic Control and Electronics, University of Craiova, A. I. Cuza Str., no. 13, 200585 Craiova, Romania.Department of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Petru Rareş, no. 2, 200349 Craiova, RomaniaDepartment of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Petru Rareş, no. 2, 200349 Craiova, RomaniaDepartment of Pathophysiology, University of Medicine and Pharmacy of Craiova, Petru Rareş, no. 2, 200349 Craiova, Romania Ultrasound (US) imaging is a widely used, non-invasive method for detecting liver tumors and assessing parenchymal changes. However, the inherent variability and noise in US images pose challenges for accurate lesion identification. This study aims to develop and evaluate a deep learning (DL) model capable of performing real-time segmentation of liver lesions in US scans. A dataset of 50 video examinations was used, from which frames were extracted and manually annotated by an experienced gastroenterologist. The segmentation process was conducted using a U-Net architecture with focal Tversky loss (FTL) to address class imbalance. Two versions of the model were trained with different FTL parameters: Model 1 (α = β = 0.5, γ = 1) and Model 2 (α = 0.7, β = 0.3, γ = 0.75). The models were assessed based on key performance metrics, including intersection over union (IoU), recall, and precision. Model 1 achieved a higher IoU score (0.84) than Model 2. Both models demonstrated inference times between 30 and 80 milliseconds, confirming their feasibility for real-time US applications. Visual analysis showed that Model 1 produced more precise and contiguous lesion segmentation, whereas Model 2 tended to separate lesions that were close together. These findings suggest that the proposed DL models are effective in real-time liver lesion segmentation in US imaging. Model 1, which utilized balanced FTL parameters, demonstrated superior segmentation accuracy. https://ami.info.umfcluj.ro/index.php/AMI/article/view/1194Ultrasound ImagingLiver TumorSegmentationU-NetFocal Tversky LossReal-time detection of PCR products |
| spellingShingle | Marinela-Cristiana URHUŢ Larisa Daniela SĂNDULESCU Costin Teodor STREBA Mădălin MĂMULEANU Adriana CIOCÂLTEU Sergiu Marian CAZACU Suzana DĂNOIU Real-Time Liver Lesion Segmentation in Ultrasound Imaging using Deep Learning Applied Medical Informatics Ultrasound Imaging Liver Tumor Segmentation U-Net Focal Tversky Loss Real-time detection of PCR products |
| title | Real-Time Liver Lesion Segmentation in Ultrasound Imaging using Deep Learning |
| title_full | Real-Time Liver Lesion Segmentation in Ultrasound Imaging using Deep Learning |
| title_fullStr | Real-Time Liver Lesion Segmentation in Ultrasound Imaging using Deep Learning |
| title_full_unstemmed | Real-Time Liver Lesion Segmentation in Ultrasound Imaging using Deep Learning |
| title_short | Real-Time Liver Lesion Segmentation in Ultrasound Imaging using Deep Learning |
| title_sort | real time liver lesion segmentation in ultrasound imaging using deep learning |
| topic | Ultrasound Imaging Liver Tumor Segmentation U-Net Focal Tversky Loss Real-time detection of PCR products |
| url | https://ami.info.umfcluj.ro/index.php/AMI/article/view/1194 |
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