Oncological image analysis

Brady, Michael; Highnam, Ralph; Irving, Benjamin; Schnabel, Julia A.

doi:10.1016/j.media.2016.06.012

Cited by 14 publications

(16 citation statements)

References 11 publications

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“…The principal contribution of research in computer science to biomedical data integration concerns the proper fusion of diverse and heterogeneous datasets [18,19]-i.e., medical imaging modalities (possibly validating radiomics-based biomarkers against histopathology [20]), Electronic Health Record (EHR) engines [21], high-throughput technologies (i.e., multi-omics studies [22]), and real-time monitoring (exploiting m-health applications)-to provide a comprehensive clinical knowledge for precision medicine [9]. Fig.…”

Section: Integrating Nature-inspired Methods Into the Clinical Workflowmentioning

confidence: 99%

“…As a matter of fact, the amount of heterogeneous biomedical data is increasing more and more thanks to the advances in image acquisition modalities and high-throughput technologies [5,6]. In addition, electronic health (e-health) [7] and mobile health (m-health) [8] can be properly integrated to support personalised screening and diagnosis [9]. Therefore, cutting-edge Information and Communication Technology (ICT) can enable the shift from organisation-centric to patient-centric models, leading to collaborative multi-institutional healthcare service delivery processes [10].…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration

Rundo

Militello

Vitabile

et al. 2019

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Natural phenomena and mechanisms have always intrigued humans, inspiring the design of effective solutions for real-world problems. Indeed, fascinating processes occur in nature, giving rise to an ever-increasing scientific interest. In everyday life, the amount of heterogeneous biomedical data is increasing more and more thanks to the advances in image acquisition modalities and high-throughput technologies. The automated analysis of these large-scale datasets creates new compelling challenges for data-driven and model-based computational methods. The application of intelligent algorithms, which mimic natural phenomena, is emerging as an effective paradigm for tackling complex problems, by considering the unique challenges and opportunities pertaining to biomedical images. Therefore, the principal contribution of computer science research in life sciences concerns the proper combination of diverse and heterogeneous datasets-i.e., medical imaging modalities (considering also radiomics approaches), Electronic Health Record engines, multi-omics studies, and real-time monitoring-to provide a comprehensive clinical knowledge. In this paper, the state-of-the-art of nature-inspired medical image

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Section: Integrating Nature-inspired Methods Into the Clinical Workflowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration

Rundo

Militello

Vitabile

et al. 2019

View full text Add to dashboard Cite

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“…It is not meant to be exhaustive but rather to highlight important aspects of the developing field. Readers interested in more general information on data analysis methods might reference some excellent reviews for data analysis in breast cancer, 7,46 general oncology, 167,246 biomedical imaging informatics, 14 translational imaging, 247 and big healthcare data management. 248…”

Section: Quantitative Multiscale Imaging Outside the Breastmentioning

confidence: 99%

Review of quantitative multiscale imaging of breast cancer

et al. 2018

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Breast cancer is the most common cancer among women worldwide and ranks second in terms of overall cancer deaths. One of the difficulties associated with treating breast cancer is that it is a heterogeneous disease with variations in benign and pathologic tissue composition, which contributes to disease development, progression, and treatment response. Many of these phenotypes are uncharacterized and their presence is difficult to detect, in part due to the sparsity of methods to correlate information between the cellular microscale and the whole-breast macroscale. Quantitative multiscale imaging of the breast is an emerging field concerned with the development of imaging technology that can characterize anatomic, functional, and molecular information across different resolutions and fields of view. It involves a diverse collection of imaging modalities, which touch large sections of the breast imaging research community. Prospective studies have shown promising results, but there are several challenges, ranging from basic physics and engineering to data processing and quantification, that must be met to bring the field to maturity. This paper presents some of the challenges that investigators face, reviews currently used multiscale imaging methods for preclinical imaging, and discusses the potential of these methods for clinical breast imaging.

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“…Classically the characterization of colon’s pathology is realized from histology 1 but is now also investigated with in vivo imaging techniques which enable the oncological 2 early detection of abnormal physiological processes such as inflammation of dysplastic lesions. This includes chromoendoscopy 3 , confocal laser endomicroscopy 4,5 or multiphoton microscopy 6 .…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy

Rasti

Wolf

Dorez

et al. 2019

Sci Rep

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In this article, we address the problem of the classification of the health state of the colon’s wall of mice, possibly injured by cancer with machine learning approaches. This problem is essential for translational research on cancer and is a priori challenging since the amount of data is usually limited in all preclinical studies for practical and ethical reasons. Three states considered including cancer, health, and inflammatory on tissues. Fully automated machine learning-based methods are proposed, including deep learning, transfer learning, and shallow learning with SVM. These methods addressed different training strategies corresponding to clinical questions such as the automatic clinical state prediction on unseen data using a pre-trained model, or in an alternative setting, real-time estimation of the clinical state of individual tissue samples during the examination. Experimental results show the best performance of 99.93% correct recognition rate obtained for the second strategy as well as the performance of 98.49% which were achieved for the more difficult first case.

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Oncological image analysis

Cited by 14 publications

References 11 publications

A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration

A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration

Review of quantitative multiscale imaging of breast cancer

Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy

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