Acceleration of brain cancer detection algorithms during surgery procedures using GPUs

Torti, Emanuele; Fontanella, Alessandro; Florimbi, Giordana; Leporati, Francesco; Fabelo, Himar; Ortega, Sebastián; Callicó, Gustavo M.

doi:10.1016/j.micpro.2018.06.005

Cited by 19 publications

(9 citation statements)

References 19 publications

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“…Moreover, these HS images were processed in approximately 1 min, achieving surgical-time processing, using a manycore platform (computing system composed of hundreds of independent processor cores, designed for high-performance parallel processing) [177,178,179,180]. However, other recent experiments published by Torti et al and Florimbi et al demonstrated that the use of graphic processing units (GPUs) could accelerate the processing of the data to only a few seconds [181,182,183].…”

Section: Medical Hyperspectral Imaging For Cancer Analysismentioning

confidence: 99%

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

Halicek

Fabelo

Ortega

et al. 2019

Cancers

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167

124

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In contrast to conventional optical imaging modalities, hyperspectral imaging (HSI) is able to capture much more information from a certain scene, both within and beyond the visual spectral range (from 400 to 700 nm). This imaging modality is based on the principle that each material provides different responses to light reflection, absorption, and scattering across the electromagnetic spectrum. Due to these properties, it is possible to differentiate and identify the different materials/substances presented in a certain scene by their spectral signature. Over the last two decades, HSI has demonstrated potential to become a powerful tool to study and identify several diseases in the medical field, being a non-contact, non-ionizing, and a label-free imaging modality. In this review, the use of HSI as an imaging tool for the analysis and detection of cancer is presented. The basic concepts related to this technology are detailed. The most relevant, state-of-the-art studies that can be found in the literature using HSI for cancer analysis are presented and summarized, both in-vivo and ex-vivo. Lastly, we discuss the current limitations of this technology in the field of cancer detection, together with some insights into possible future steps in the improvement of this technology.

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Section: Medical Hyperspectral Imaging For Cancer Analysismentioning

confidence: 99%

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

Halicek

Fabelo

Ortega

et al. 2019

Cancers

Self Cite

167

124

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“…Fourier coefficients [13], normalized difference nuclear index [14], sparse representation [15], box-plot and the watershed method [16], superpixel method [9], markov random fields [17,18], and morphological method [19], were used for hyperspectral image processing and quantification analysis; (3) Machine learning techniques. Many of the advancements have been done in cancer identification using traditional machine learning classification models, such as linear discriminant analysis [20][21][22][23][24][25][26], quadratic discriminant analysis [21], support vector machine [12,17,[20][21][22][27][28][29][30][31][32][33][34][35][36][37], decision trees [22], k-nearest neighbors algorithm [22,38], k-means [12,19,39], naïve bayes [22], random forests [21,22,34,37], maximum likelihood [40], minimum spanning forest [31], gaussian m...…”

Section: Introductionmentioning

confidence: 99%

Adaptive deep learning for head and neck cancer detection using hyperspectral imaging

Wang

et al. 2019

Vis. Comput. Ind. Biomed. Art

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It can be challenging to detect tumor margins during surgery for complete resection. The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissue adaptively for cancer detection on hyperspectral images in an animal model. Specifically, an auto-encoder network is trained based on the wavelength bands on hyperspectral images to extract the deep information to create a pixel-wise prediction of cancerous and benign pixel. According to the output hypothesis of each pixel, the misclassified pixels would be reclassified in the right prediction direction based on their adaptive weights. The auto-encoder network is again trained based on these updated pixels. The learner can adaptively improve the ability to identify the cancer and benign tissue by focusing on the misclassified pixels, and thus can improve the detection performance. The adaptive deep learning method highlighting the tumor region proved to be accurate in detecting the tumor boundary on hyperspectral images and achieved a sensitivity of 92.32% and a specificity of 91.31% in our animal experiments. This adaptive learning method on hyperspectral imaging has the potential to provide a noninvasive tool for tumor detection, especially, for the tumor whose margin is indistinct and irregular.

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“…Since the main goal of most works in the state of the art is to execute this algorithm in real-time, or at least with reduced execution times, it becomes necessary to exploit the possibilities offered by high performance devices, being GPUs a highly appealing option. As a massively parallel architecture, this kind of devices has been widely used for exploiting data parallelism in several applications from different scientific fields [ 14 , 15 , 16 ], as well as in HSI [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classification of Human Brain Tumor in Hyperspectral Images

Florimbi

Fabelo

Torti

et al. 2018

Sensors

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The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation.

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Acceleration of brain cancer detection algorithms during surgery procedures using GPUs

Cited by 19 publications

References 19 publications

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

Adaptive deep learning for head and neck cancer detection using hyperspectral imaging

Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classification of Human Brain Tumor in Hyperspectral Images

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