Statistical Parameter-based Automatic Liver Tumor Segmentation from Abdominal CT Scans: A Potiential Radiomic Signature

Kumar, Y. Rakesh; Muthukrishnan, N. Moorthy; Mahajan, Abhishek; Kurapati.Priyanka, Patnala S.R Chandra Murthy; Padmavathi, G.; Nethra, M.; Sneha, R.; Thakur, Meenakshi

doi:10.1016/j.procs.2016.07.232

Cited by 14 publications

(4 citation statements)

References 10 publications

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“…Segmentation of an ROI can be carried out manually, semi-automated, or fully automated. Comparative studies have been reported to increase the performance of fully automated, manual, or semiautomated segmentation of these images (Egger et al, 2013;Velazquez et al, 2013Velazquez et al, , 2015Kumar et al, 2016;Narang et al, 2016). The 3D-Slicer is a free open source segmentation software for biomedical research that is widely adopted in the medical field .…”

Section: Fig 1 a Typical Workflow Of Radiomicsmentioning

confidence: 99%

Towards precision medicine: from quantitative imaging to radiomics

Acharya

Hagiwara

Sudarshan

et al. 2018

J. Zhejiang Univ. Sci. B

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Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.

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Section: Fig 1 a Typical Workflow Of Radiomicsmentioning

confidence: 99%

Towards precision medicine: from quantitative imaging to radiomics

Acharya

Hagiwara

Sudarshan

et al. 2018

J. Zhejiang Univ. Sci. B

View full text Add to dashboard Cite

show abstract

“…[124][125][126][127] • Lung nodule segmentation on computed tomography (CT). [128][129][130] • Liver tumor segmentation on CT. 130,131 • Prostate gland tumor detection on magnetic resonance imaging (MRI). 112,132,133 • Brain tumor survival prediction [134][135][136] : • Glioblastoma recurrence prediction.…”

Section: Applications Of Artificial Intelligence In Oncologymentioning

confidence: 99%

Multisystem Imaging Recommendations/Guidelines: In the Pursuit of Precision Oncology

Mahajan

Chakrabarty

Majithia

et al. 2023

Indian J Med Paediatr Oncol

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With an increasing rate of cancers in almost all age groups and advanced screening techniques leading to an early diagnosis and longer longevity of patients with cancers, it is of utmost importance that radiologists assigned with cancer imaging should be prepared to deal with specific expected and unexpected circumstances that may arise during the lifetime of these patients. Tailored integration of preventive and curative interventions with current health plans and global escalation of efforts for timely diagnosis of cancers will pave the path for a cancer-free world. The commonly encountered circumstances in the current era, complicating cancer imaging, include coronavirus disease 2019 infection, pregnancy and lactation, immunocompromised states, bone marrow transplant, and screening of cancers in the relevant population. In this article, we discuss the imaging recommendations pertaining to cancer screening and diagnosis in the aforementioned clinical circumstances.

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“…Brain tumour segmentation (11)(12)(13)(14). c. Lung nodule segmentation on computed tomography (CT) (15)(16)(17). d. Liver tumour segmentation on CT (17,18).…”

Section: Systematic Review and Meta-analysis Datamentioning

confidence: 99%

“…c. Lung nodule segmentation on computed tomography (CT) (15)(16)(17). d. Liver tumour segmentation on CT (17,18). e. Prostate gland tumour detection on magnetic resonance imaging (MRI) (19,20).…”

Section: Systematic Review and Meta-analysis Datamentioning

confidence: 99%

Editorial: The use of deep learning in mapping and diagnosis of cancers

Mahajan

Chakrabarty

2022

Front. Oncol.

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Editorial on the Research TopicThe use of deep learning in mapping and diagnosis of cancers Deep Learning (DL) is a subset and an augmented version of Machine Learning (ML), which in turn is a subgroup of Artificial Intelligence (AI), that uses layers of neural networks, similar to human brain, for performing complex tasks quickly and accurately. AI can recognize patterns in a large volume of data and extract characteristics imperceptible to the human eye (1). Convolutional Neural Network (CNN) is the most commonly used network of DL, which contains multiple layers, with weighted connections between neurons that are trained iteratively to improve performance. DL can be supervised or unsupervised, but most of the practical uses of DL in cancer has been with supervised learning where labelled images are used for data training (2). Despite the growing number of uses of DL in cancer mapping and diagnosis, there are uncharted territories in DL which remain to be explored to utilize it to its full capacity. Also, in spite of the revolution in cancer research that DL has ushered in, there are a lot of challenges to overcome, before DL can be widely used and accepted in every corner of the world. Role of DL in oncologyThere has been an unprecedented surge in DL based research in oncology due to the availability of big data, powerful hardware and robust algorithms. Screening and diagnosis of cancer, prediction of treatment response, and survival outcome and recurrence prediction, are the various roles of ML and DL in cancer management. AI algorithms integrated with clinical decision support (CDS) tools can automatically mine electronic health record (EHR) and identify cohort that would benefit maximum from Frontiers in Oncology frontiersin.org 01

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Statistical Parameter-based Automatic Liver Tumor Segmentation from Abdominal CT Scans: A Potiential Radiomic Signature

Cited by 14 publications

References 10 publications

Towards precision medicine: from quantitative imaging to radiomics

Towards precision medicine: from quantitative imaging to radiomics

Multisystem Imaging Recommendations/Guidelines: In the Pursuit of Precision Oncology

Editorial: The use of deep learning in mapping and diagnosis of cancers

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