Ensemble Learning Based Segmentation of Metastatic Liver Tumours in Contrast-Enhanced Computed Tomography

Shimizu, A.; Narihira, Takuya; Kobatake, Hidefumi; Furukawa, Daisuke; Nakamura, Shigeru; Shinozaki, K.

doi:10.1587/transinf.e96.d.864

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Sixty studies had lesion segmentation as a primary or secondary study aim. Thirty-six are journal articles [ 24 , 29 , 31 , 32 , 38 , 46 , 47 , 55 , 56 , 62 , 72 , 78 , 84 , 91 , 93 , 94 , 97 , 98 , 102 , 111 , 115 , 117 , 118 , 122 , 124 , 125 , 130 , 133 – 135 , 137 , 138 , 140 , 201 ], and twenty-four [ 22 , 37 , 42 , 64 , 65 , 68 , 82 , 88 , 92 , 96 , 99 , 103 , 108 , 121 , 124 , 126 – 129 , 131 , 132 , 136 , 139 , 200 ] are proceedings papers.…”

Section: Resultsmentioning

confidence: 99%

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

Radiya,

Joakimsen,

Mikalsen

et al. 2023

Eur Radiol

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Objectives Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging? Methods A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography. Results One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians’ intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy. Conclusion Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this. Key Points • ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.

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Section: Resultsmentioning

confidence: 99%

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

Radiya,

Joakimsen,

Mikalsen

et al. 2023

Eur Radiol

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Understanding Medical Images Based on Computational Anatomy Models

Hanaoka

Kamiya

Sato

et al. 2017

Computational Anatomy Based on Whole Body Imaging

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Unsupervised detection of liver lesions in CT images

Afifi

Nakaguchi

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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This work presents an automatic approach for liver lesions detection in CT images. In this approach, liver is first segmented using fast and reliable semi-automatic technique. After liver segmentation, lesion detection is formulated as an unsupervised segmentation approach to alleviate tedious user interaction or prior learning requirements. The Meanshift clustering technique is utilized to separate different liver tissues in each CT slice. Consequently, a rule-based system is proposed to automatically and dynamically estimate healthy and unhealthy tissues distributions, and produces initial estimation of defected tissue. Finally, the graph cuts algorithm is employed to refine the initial detection and produces the finial lesions. Validation of the proposed approach using 15 patients' CT data shows high detection rate of 93%, which makes it an efficient initial opinion in the diagnosis process.

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Ensemble Learning Based Segmentation of Metastatic Liver Tumours in Contrast-Enhanced Computed Tomography

Cited by 3 publications

References 11 publications

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

Understanding Medical Images Based on Computational Anatomy Models

Unsupervised detection of liver lesions in CT images

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