Real-Time Deep Recognition of Standardized Liver Ultrasound Scan Locations
Liver ultrasound (US) plays a critical role in diagnosing liver diseases. However, it is often difficult for examiners to accurately identify the liver segments captured in US images due to patient variability and the complexity of US images. Our study aim is automatic, real-time recognition of stan...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/10/4850 |
| _version_ | 1797598366967791616 |
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| author | Jonghwan Shin Sukhan Lee Juneho Yi |
| author_facet | Jonghwan Shin Sukhan Lee Juneho Yi |
| author_sort | Jonghwan Shin |
| collection | DOAJ |
| description | Liver ultrasound (US) plays a critical role in diagnosing liver diseases. However, it is often difficult for examiners to accurately identify the liver segments captured in US images due to patient variability and the complexity of US images. Our study aim is automatic, real-time recognition of standardized US scans coordinated with reference liver segments to guide examiners. We propose a novel deep hierarchical architecture for classifying liver US images into 11 standardized US scans, which has yet to be properly established due to excessive variability and complexity. We address this problem based on a hierarchical classification of 11 US scans with different features applied to individual hierarchies as well as a novel feature space proximity analysis for handling ambiguous US images. Experiments were performed using US image datasets obtained from a hospital setting. To evaluate the performance under patient variability, we separated the training and testing datasets into distinct patient groups. The experimental results show that the proposed method achieved an F1-score of more than 93%, which is more than sufficient for a tool to guide examiners. The superior performance of the proposed hierarchical architecture was demonstrated by comparing its performance with that of non-hierarchical architecture. |
| first_indexed | 2024-03-11T03:20:11Z |
| format | Article |
| id | doaj.art-2dd7d90e1ab7482fa16234aaede2cf3d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:20:11Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2dd7d90e1ab7482fa16234aaede2cf3d2023-11-18T03:13:33ZengMDPI AGSensors1424-82202023-05-012310485010.3390/s23104850Real-Time Deep Recognition of Standardized Liver Ultrasound Scan LocationsJonghwan Shin0Sukhan Lee1Juneho Yi2Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaArtificial Intelligence Department, Sungkyunkwan University, Suwon 16419, Republic of KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaLiver ultrasound (US) plays a critical role in diagnosing liver diseases. However, it is often difficult for examiners to accurately identify the liver segments captured in US images due to patient variability and the complexity of US images. Our study aim is automatic, real-time recognition of standardized US scans coordinated with reference liver segments to guide examiners. We propose a novel deep hierarchical architecture for classifying liver US images into 11 standardized US scans, which has yet to be properly established due to excessive variability and complexity. We address this problem based on a hierarchical classification of 11 US scans with different features applied to individual hierarchies as well as a novel feature space proximity analysis for handling ambiguous US images. Experiments were performed using US image datasets obtained from a hospital setting. To evaluate the performance under patient variability, we separated the training and testing datasets into distinct patient groups. The experimental results show that the proposed method achieved an F1-score of more than 93%, which is more than sufficient for a tool to guide examiners. The superior performance of the proposed hierarchical architecture was demonstrated by comparing its performance with that of non-hierarchical architecture.https://www.mdpi.com/1424-8220/23/10/4850ultrasound imageliver scandeep learninghierarchical classification |
| spellingShingle | Jonghwan Shin Sukhan Lee Juneho Yi Real-Time Deep Recognition of Standardized Liver Ultrasound Scan Locations Sensors ultrasound image liver scan deep learning hierarchical classification |
| title | Real-Time Deep Recognition of Standardized Liver Ultrasound Scan Locations |
| title_full | Real-Time Deep Recognition of Standardized Liver Ultrasound Scan Locations |
| title_fullStr | Real-Time Deep Recognition of Standardized Liver Ultrasound Scan Locations |
| title_full_unstemmed | Real-Time Deep Recognition of Standardized Liver Ultrasound Scan Locations |
| title_short | Real-Time Deep Recognition of Standardized Liver Ultrasound Scan Locations |
| title_sort | real time deep recognition of standardized liver ultrasound scan locations |
| topic | ultrasound image liver scan deep learning hierarchical classification |
| url | https://www.mdpi.com/1424-8220/23/10/4850 |
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