Liver tissue classification of en face images by fractal dimension‐based support vector machine

Zhu, Yue; Gao, Wanrong; Guo, Zhenyan; Ya, Zhou; Zhou, Yuan

doi:10.1002/jbio.201960154

Cited by 10 publications

(8 citation statements)

References 49 publications

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“…Full-field optical coherence tomography (FF-OCT) has been recognized for its label-free subcellular imaging capabilities. Recently, Zhu et al [ 90 ] demonstrated the promise of their classifier using FF-OCT images for label-free, quantified tumor detection. This suggests that a fractal dimension-based classifier could potentially assist clinicians in identifying tumor boundaries for surgical resection in the future.…”

Section: The Fractal Nature Of the Gastrointestinal Systemmentioning

confidence: 99%

Fractal nature of human gastrointestinal system: Exploring a new era

Grizzi¹,

Spadaccini²,

Chiriva‐Internati³

et al. 2023

World J Gastroenterol

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The morphological complexity of cells and tissues, whether normal or pathological, is characterized by two primary attributes: Irregularity and self-similarity across different scales. When an object exhibits self-similarity, its shape remains unchanged as the scales of measurement vary because any part of it resembles the whole. On the other hand, the size and geometric characteristics of an irregular object vary as the resolution increases, revealing more intricate details. Despite numerous attempts, a reliable and accurate method for quantifying the morphological features of gastrointestinal organs, tissues, cells, their dynamic changes, and pathological disorders has not yet been established. However, fractal geometry, which studies shapes and patterns that exhibit self-similarity, holds promise in providing a quantitative measure of the irregularly shaped morphologies and their underlying self-similar temporal behaviors. In this context, we explore the fractal nature of the gastrointestinal system and the potential of fractal geometry as a robust descriptor of its complex forms and functions. Additionally, we examine the practical applications of fractal geometry in clinical gastroenterology and hepatology practice.

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Section: The Fractal Nature Of the Gastrointestinal Systemmentioning

confidence: 99%

Fractal nature of human gastrointestinal system: Exploring a new era

Grizzi¹,

Spadaccini²,

Chiriva‐Internati³

et al. 2023

World J Gastroenterol

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“…(I-K) Boxplot of selected features for the Mean, Kurtosis, and FracDim. mean and kurtosis are not sensitive in distinguishing between normal and cancerous hepatocytes, the mean and kurtosis are not sensitive in distinguishing between normal and cancerous hepatocytes, when the liver becomes cancerous, the value of the fractal parameter increases ( 88 ). Copyright 2020, Wiley.…”

Section: Application Of Oct In Oncologymentioning

confidence: 99%

“…They used the label-free human liver tomography stack to extract seven quantitative parameters, including mean, variance, skewness, kurtosis, energy, entropy, and fractal dimension (FD). The value of FD grows as the liver becomes cancerous, signifying that a divided-dimensional classifier can be utilized for label-free quantitative tumor detection ( 88 ).…”

Section: Application Of Oct In Oncologymentioning

confidence: 99%

Research progress on the application of optical coherence tomography in the field of oncology

et al. 2022

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Optical coherence tomography (OCT) is a non-invasive imaging technique which has become the “gold standard” for diagnosis in the field of ophthalmology. However, in contrast to the eye, nontransparent tissues exhibit a high degree of optical scattering and absorption, resulting in a limited OCT imaging depth. And the progress made in the past decade in OCT technology have made it possible to image nontransparent tissues with high spatial resolution at large (up to 2mm) imaging depth. On the one hand, OCT can be used in a rapid, noninvasive way to detect diseased tissues, organs, blood vessels or glands. On the other hand, it can also identify the optical characteristics of suspicious parts in the early stage of the disease, which is of great significance for the early diagnosis of tumor diseases. Furthermore, OCT imaging has been explored for imaging tumor cells and their dynamics, and for the monitoring of tumor responses to treatments. This review summarizes the recent advances in the OCT area, which application in oncological diagnosis and treatment in different types: (1) superficial tumors:OCT could detect microscopic information on the skin’s surface at high resolution and has been demonstrated to help diagnose common skin cancers; (2) gastrointestinal tumors: OCT can be integrated into small probes and catheters to image the structure of the stomach wall, enabling the diagnosis and differentiation of gastrointestinal tumors and inflammation; (3) deep tumors: with the rapid development of OCT imaging technology, it has shown great potential in the diagnosis of deep tumors such in brain tumors, breast cancer, bladder cancer, and lung cancer.

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“…In a recent study, images from full-field optical coherence tomography were used to develop an SVM model that recognizes hepatic cancerous cells as a tool to detect tumor boundaries for resection intraoperatively[ 117 ]. A different study used X-ray imaging to develop a CNN model as the basis of a framework that automatically detects fiducial markers, performs 3D position reconstruction, and evaluates intrafraction motion during stereotactic body radiation therapy for liver malignancies[ 118 ].…”

Section: Applications Of Ai In Hcc Managementmentioning

confidence: 99%

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Christou¹,

Tsoulfas²

2022

WJGO

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Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.

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Liver tissue classification of en face images by fractal dimension‐based support vector machine

Cited by 10 publications

References 49 publications

Fractal nature of human gastrointestinal system: Exploring a new era

Fractal nature of human gastrointestinal system: Exploring a new era

Research progress on the application of optical coherence tomography in the field of oncology

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

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