Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection

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In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection

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.Comment: 19 pages, 12 figure

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area

In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection

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

Ozone therapy versus surgery for lumbar disc herniation : A randomized double-blind controlled trial

Altres ajuts: Ministerio de Sanidad (Madrid); Ilustre Colegio de Médicos de Las Palmas (I19/18).Objectives: Surgery is the treatment of choice for symptomatic disc herniation after conservative management. Several studies have suggested the potential utility of intradiscal ozone infiltration in this pathology. The aim of this trial was to compare intradiscal ozone infiltration vs. oxygen infiltration vs. surgery. Design and interventions: This was a randomized, double-blinded, and controlled trial in patients on a waiting list for herniated disc surgery. There were three treatment groups: surgery; intradiscal ozone infiltration (plus foraminal infiltration of ozone, steroids, and anesthetic); intradiscal oxygen infiltration (plus foraminal infiltration of oxygen, steroids, and anesthetic). Main outcome measures: The requirements for surgery. Results: Five years after the treatment of the last recruited patient (median follow-up: 78 months), the requirement for further surgery was 20 % for patients in the ozone group and 60 % for patients in the oxygen group. 11 % of patients initially treated with surgery also required a second surgery. Compared to the surgery group, the ozone group showed: 1) significantly lower number of inpatient days: median 3 days (interquartile range: 3-3.5 days) vs. 0 days (interquartile range: 0-1.5 days), p = 0.012; 2) significantly lower costs: median EUR 3702 (interquartile range: EUR 3283-7630) vs. EUR 364 (interquartile range: EUR 364-2536), p = 0.029. Conclusions: Our truncated trial showed that intradiscal ozone infiltrations decreased the requirements for conventional surgery, resulting in decreased hospitalization durations and associated costs. These findings and their magnitude are of interest to patients and health services providers. Further validation is ongoing

Spinal cord stimulation as adjuvant during chemotherapy and reirradiation treatment of recurrent high-grade gliomas

AIMS: Relapsed high-grade gliomas (HGGs) have poor prognoses and there is no standard treatment. HGGs have ischemia/hypoxia associated and, as such, drugs and oxygen have low access, with increased resistance to chemotherapy and radiotherapy. Tumor hypoxia modification can improve outcomes and overall survival in some patients with these tumors. In previous works, we have described that cervical spinal cord stimulation can modify tumor microenvironment in HGG by increasing tumor blood flow, oxygenation, and metabolism. The aim of this current, preliminary, nonrandomized, study was to assess the clinical effect of spinal cord stimulation during brain reirradiation and chemotherapy deployed for the treatment of recurrent HGG; the hypothesis being that an improvement in oxygenated blood supply would facilitate enhanced delivery of the scheduled therapy. MATERIALS AND METHODS: Seven patients had spinal cord stimulation applied during the scheduled reirradiation and chemotherapy for the treatment of recurrent HGG (6 anaplastic gliomas and 1 glioblastoma). Median dose of previous irradiation was 60 Gy (range = 56-72 Gy) and median dose of reirradiation was 46 Gy (range = 40-46 Gy). Primary end point of the study was overall survival (OS) following confirmation of HGG relapse. RESULTS: From the time of diagnosis of last tumor relapse before reirradiation, median OS was 39 months (95% CI = 0-93) for the overall study group: 39 months (95% CI = 9-69) for those with anaplastic gliomas and 16 months for the patient with glioblastoma. Posttreatment, doses of corticosteroids was significantly decreased (P = .026) and performance status significantly improved (P = .046). CONCLUSIONS: Spinal cord stimulation during reirradiation and chemotherapy is feasible and well tolerated. In our study, spinal cord stimulation was associated with clinical improvement and longer survival than previously reported in recurrent anaplastic gliomas. Spinal cord stimulation as adjuvant during chemotherapy and reirradiation in relapsed HGGs merits further research

VNIR–NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection

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

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