Hyperspectral diagnostic imaging of the cervix: initial observations

Parker, Megan N.; Karins, James P.; O’Connor, Dennis M.

doi:10.1109/pacmed.1998.767958

Cited by 5 publications

(3 citation statements)

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“…As cancer involves a change in cellular pathology associated with different chemical signature [14], it should be detected as a change in the HS signature of the tissue. HSI has been used to detect different types of cancer, such us cervix [15], breast [16]- [18], colon [19]- [22], gastrointestinal [23], skin [24], [25], prostate [26], [27], oral tissue [28] and tongue [29]- [31] cancers. Regarding HSI to assist brain surgery, the work performed by this research group in [32] is the first study reported to the best of authors' knowledge.…”

Section: Introductionmentioning

confidence: 99%

In-VivoHyperspectral Human Brain Image Database for Brain Cancer Detection

et al. 2019

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The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral algorithms for medical applications is the lack of specific, publicly available, and hyperspectral medical data. The work described in this paper was developed within the framework of the European project HELICoiD (HypErspectraL Imaging Cancer Detection), which had as a main goal the application of hyperspectral imaging to the delineation of brain tumors in real-time during neurosurgical operations. In this paper, the methodology followed to generate the first hyperspectral database of in-vivo human brain tissues is presented. Data was acquired employing a customized hyperspectral acquisition system capable of capturing information in the Visual and Near InfraRed (VNIR) range from 400 to 1000 nm. Repeatability was assessed for the cases where two images of the same scene were captured consecutively. The analysis reveals that the system works more efficiently in the spectral range between 450 and 900 nm. A total of 36 hyperspectral images from 22 different patients were obtained. From these data, more than 300 000 spectral signatures were labeled employing a semi-automatic methodology based on the spectral angle mapper algorithm. Four different classes were defined: normal tissue, tumor tissue, blood vessel, and background elements. All the hyperspectral data has been made available in a public repository. INDEX TERMS Hyperspectral imaging, cancer detection, biomedical imaging, medical diagnostic imaging, image databases. The associate editor coordinating the review of this manuscript and approving it for publication was Bora Onat.

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Section: Introductionmentioning

confidence: 99%

In-VivoHyperspectral Human Brain Image Database for Brain Cancer Detection

et al. 2019

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“…On the other hand, there is an alternative technique based on tissue level known as hyperspectral diagnostic imaging (HSDI). The surface of the cervix is scanned with ultraviolet and white light for detecting lesions [ 25 – 27 ]. The scanning is achieved one line at a time, with the scan time varying from 12 to 24 seconds.…”

Section: Screening For Cervical Carcinomamentioning

confidence: 99%

Intelligent Screening Systems for Cervical Cancer

Jusman

Osman

2014

The Scientific World Journal

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Advent of medical image digitalization leads to image processing and computer-aided diagnosis systems in numerous clinical applications. These technologies could be used to automatically diagnose patient or serve as second opinion to pathologists. This paper briefly reviews cervical screening techniques, advantages, and disadvantages. The digital data of the screening techniques are used as data for the computer screening system as replaced in the expert analysis. Four stages of the computer system are enhancement, features extraction, feature selection, and classification reviewed in detail. The computer system based on cytology data and electromagnetic spectra data achieved better accuracy than other data.

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“…However, in the past decades, HSI has been commonly used in other fields, such as drug analysis [ 24 , 25 ], food quality inspection [ 26 , 27 , 28 , 29 ] or defense and security [ 30 , 31 ], among many others. In the medical field, this imaging modality has been used for research purposes since the 1990s [ 32 , 33 , 34 ]. However, in recent years, medical HSI has started to achieve promising results with respect to cancer detection [ 17 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Framework for In Vivo Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain

Fabelo

Halicek

Ortega

et al. 2019

Sensors

124

127

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The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imaging is a non-contact, non-ionizing, and label-free imaging modality that can assist surgeons during this challenging task without using any contrast agent. In this work, we present a deep learning-based framework for processing hyperspectral images of in vivo human brain tissue. The proposed framework was evaluated by our human image database, which includes 26 in vivo hyperspectral cubes from 16 different patients, among which 258,810 pixels were labeled. The proposed framework is able to generate a thematic map where the parenchymal area of the brain is delineated and the location of the tumor is identified, providing guidance to the operating surgeon for a successful and precise tumor resection. The deep learning pipeline achieves an overall accuracy of 80% for multiclass classification, improving the results obtained with traditional support vector machine (SVM)-based approaches. In addition, an aid visualization system is presented, where the final thematic map can be adjusted by the operating surgeon to find the optimal classification threshold for the current situation during the surgical procedure.

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Hyperspectral diagnostic imaging of the cervix: initial observations

Cited by 5 publications

References 1 publication

In-VivoHyperspectral Human Brain Image Database for Brain Cancer Detection

In-VivoHyperspectral Human Brain Image Database for Brain Cancer Detection

Intelligent Screening Systems for Cervical Cancer

Deep Learning-Based Framework for In Vivo Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain

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