Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation

Jiang, Huiyan; Li, Siqi

doi:10.1155/2018/8536854

Cited by 8 publications

(2 citation statements)

References 34 publications

(36 reference statements)

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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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“…In addition, the density of the liver tissues is highly similar to the densities of many other types of soft tissues in the abdominal cavity [ 11 ]. Moreover, medical CT imaging often produces images of low contrast and uneven grey scales, making it difficult to accurately segment liver images [ 12 ]. In short, liver segmentation in CT images has become a major challenge as it can hardly achieve the desired or expected outcomes.…”

Section: Introductionmentioning

confidence: 99%

Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures

Wei

Chen

Lai

et al. 2021

BioMed Research International

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Liver image segmentation has been increasingly employed for key medical purposes, including liver functional assessment, disease diagnosis, and treatment. In this work, we introduce a liver image segmentation method based on generative adversarial networks (GANs) and mask region-based convolutional neural networks (Mask R-CNN). Firstly, since most resulting images have noisy features, we further explored the combination of Mask R-CNN and GANs in order to enhance the pixel-wise classification. Secondly, k -means clustering was used to lock the image aspect ratio, in order to get more essential anchors which can help boost the segmentation performance. Finally, we proposed a GAN Mask R-CNN algorithm which achieved superior performance in comparison with the conventional Mask R-CNN, Mask-CNN, and k -means algorithms in terms of the Dice similarity coefficient (DSC) and the MICCAI metrics. The proposed algorithm also achieved superior performance in comparison with ten state-of-the-art algorithms in terms of six Boolean indicators. We hope that our work can be effectively used to optimize the segmentation and classification of liver anomalies.

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MS-UNet: A multi-scale UNet with feature recalibration approach for automatic liver and tumor segmentation in CT images

Kushnure

Talbar²

2021

Computerized Medical Imaging and Graphics

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Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation

Cited by 8 publications

References 34 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

Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures

MS-UNet: A multi-scale UNet with feature recalibration approach for automatic liver and tumor segmentation in CT images

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