A Higher-Order Neural Network Design for Improving Segmentation Performance in Medical Image Series

Selvi, E.; Selver, M. Alper; Güzeliş, Cüneyt; Dıcle, Oğuz

doi:10.1088/1742-6596/490/1/012079

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…The aim of liver segmentation was the primary or secondary study aim in eighty-four of the included studies. Of those, fifty-one are journal articles [ 20 , 24 , 29 – 35 , 38 – 41 , 43 – 47 , 49 , 55 – 58 , 62 , 63 , 65 , 68 , 70 – 79 , 81 , 84 – 87 , 89 , 91 , 93 – 95 , 97 , 98 , 196 , 197 ], and 33 are proceeding papers [ 19 , 21 – 23 , 25 , 26 , 36 , 37 , 42 , 48 , 51 , 53 , 54 , 59 – 61 , 64 , 66 , 67 , 69 , 80 , 82 , 83 , 88 , 90 , 92 , 96 , 99 , 100 , 103 , 198 ]. The liver segmentation was done from the CT as a whole liver, not the clinical segmentation, e.g., Couinaud segments of the liver.…”

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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