Structure convolutional extreme learning machine and case-based shape template for HCC nucleus segmentation

Li, Siqi; Jiang, Huiyan; Yao, Yu-Dong; Pang, Wenbo; Sun, Qingjiao; Kuang, Li

doi:10.1016/j.neucom.2018.05.013

Cited by 10 publications

(5 citation statements)

References 42 publications

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“…Another classification CNN system of four liver stages by Bharti et al[34] had an accuracy of 96.6%. Other studies showed that the proposed method had superior performance when compared with related works by other techniques[35,36,40,41]. Li et al[35] showed that external validation of the proposed method multiple fully connected CNN with extreme learning machine model they created by using Hep-2 cells indicates that their method can be generalised in grading HCC nuclei.…”

Section: Resultsmentioning

confidence: 88%

“…Table 2 summarises details of the 11 studies included[31-41]. The studies demonstrated the ability of CNN models in analysed images of liver cancers as follows: Classification of liver masses into five categories: category A: Classic HCC; category B: Malignant liver tumour other than HCC; category C: intermediate masses (early HCC, dysplastic nodules, or benign liver masses; category D: Haemangiomas; category E: Cysts[31], detection of small metastasis in the liver[32], discrimination between primary liver cancer (HCC) and secondaries in the liver[33], differentiation between chronic liver diseases such as cirrhosis and the presence of HCC on top of cirrhosis[34], classification of grade of HCC nuclei and segmentation of HCC nuclei on pathology images[35,36], classification of liver lesions[31,33,34,37,41], and detection of liver tumour or liver masses and identification of their types and phases[38-40]. While these studies examined liver CT images[31,32,37-40], ultrasound images[34], and 3D multi-parameter MRI scan images[33,41], other images such as cellular and histopathological images were also included[35,36].…”

Section: Resultsmentioning

confidence: 99%

“…The remaining 50 authors were non-medical and were from engineering, computer science, and medical image processing laboratory. Except for Radiology [31], and Ultrasonic Imaging [34], the majority of these articles were published in journals specialised in computer science and Biomedical informatics, such as Neurocomputing [32,36,37], IEEE Journal of Biomedical Health Informatics [33], Computers in Biology and Medicine [35], Medical and Biological Engineering and Computing [38], and International Journal of Computer Assisted Radiology and Surgery [39]. These two factors may explain the dominance of technical information regarding computer science information in the study methods.…”

Section: Resultsmentioning

confidence: 99%

“…The images datasets comprised 48 normal liver, 50 chronic liver, 50 cirrhosis, and 41 HCC evolved over cirrhosis. Other datasets comprised experimental data involving 127 liver pathology images[35,36].…”

Section: Resultsmentioning

confidence: 99%

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Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review

Azer

2019

WJGO

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BACKGROUNDArtificial intelligence, such as convolutional neural networks (CNNs), has been used in the interpretation of images and the diagnosis of hepatocellular cancer (HCC) and liver masses. CNN, a machine-learning algorithm similar to deep learning, has demonstrated its capability to recognise specific features that can detect pathological lesions.AIMTo assess the use of CNNs in examining HCC and liver masses images in the diagnosis of cancer and evaluating the accuracy level of CNNs and their performance.METHODSThe databases PubMed, EMBASE, and the Web of Science and research books were systematically searched using related keywords. Studies analysing pathological anatomy, cellular, and radiological images on HCC or liver masses using CNNs were identified according to the study protocol to detect cancer, differentiating cancer from other lesions, or staging the lesion. The data were extracted as per a predefined extraction. The accuracy level and performance of the CNNs in detecting cancer or early stages of cancer were analysed. The primary outcomes of the study were analysing the type of cancer or liver mass and identifying the type of images that showed optimum accuracy in cancer detection.RESULTSA total of 11 studies that met the selection criteria and were consistent with the aims of the study were identified. The studies demonstrated the ability to differentiate liver masses or differentiate HCC from other lesions (n = 6), HCC from cirrhosis or development of new tumours (n = 3), and HCC nuclei grading or segmentation (n = 2). The CNNs showed satisfactory levels of accuracy. The studies aimed at detecting lesions (n = 4), classification (n = 5), and segmentation (n = 2). Several methods were used to assess the accuracy of CNN models used.CONCLUSIONThe role of CNNs in analysing images and as tools in early detection of HCC or liver masses has been demonstrated in these studies. While a few limitations have been identified in these studies, overall there was an optimal level of accuracy of the CNNs used in segmentation and classification of liver cancers images.

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Section: Resultsmentioning

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review

Azer

2019

WJGO

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“…Li et al. ( 18 ) proposed a structure convolutional extreme learning machine and case-based shape template methods for HCC nucleus segmentation. However, various tissue structure and cell characteristics should be comprehensively considered for the diagnosis and histological grade of HCC.…”

Section: Introductionmentioning

confidence: 99%

Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma

Shi

Liang

et al. 2021

Front. Oncol.

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BackgroundAn accurate pathological diagnosis of hepatocellular carcinoma (HCC), one of the malignant tumors with the highest mortality rate, is time-consuming and heavily reliant on the experience of a pathologist. In this report, we proposed a deep learning model that required minimal noise reduction or manual annotation by an experienced pathologist for HCC diagnosis and classification.MethodsWe collected a whole-slide image of hematoxylin and eosin-stained pathological slides from 592 HCC patients at the First Affiliated Hospital, College of Medicine, Zhejiang University between 2015 and 2020. We propose a noise-specific deep learning model. The model was trained initially with 137 cases cropped into multiple-scaled datasets. Patch screening and dynamic label smoothing strategies are adopted to handle the histopathological liver image with noise annotation from the perspective of input and output. The model was then tested in an independent cohort of 455 cases with comparable tumor types and differentiations.ResultsExhaustive experiments demonstrated that our two-step method achieved 87.81% pixel-level accuracy and 98.77% slide-level accuracy in the test dataset. Furthermore, the generalization performance of our model was also verified using The Cancer Genome Atlas dataset, which contains 157 HCC pathological slides, and achieved an accuracy of 87.90%.ConclusionsThe noise-specific histopathological classification model of HCC based on deep learning is effective for the dataset with noisy annotation, and it significantly improved the pixel-level accuracy of the regular convolutional neural network (CNN) model. Moreover, the model also has an advantage in detecting well-differentiated HCC and microvascular invasion.

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Nuclei and glands instance segmentation in histology images: a narrative review

2022

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Structure convolutional extreme learning machine and case-based shape template for HCC nucleus segmentation

Cited by 10 publications

References 42 publications

Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review

Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review

Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma

Nuclei and glands instance segmentation in histology images: a narrative review

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