Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.
We present
gSUPPOSe, a novel, to the best
of our knowledge, gradient-based implementation of the SUPPOSe
algorithm that we have developed for the localization of single
emitters. We study the performance of gSUPPOSe and compressed sensing
STORM (CS-STORM) on
simulations of single-molecule localization microscopy (SMLM) images
at different fluorophore densities and in a wide range of
signal-to-noise ratio conditions. We also study the combination of
these methods with prior image denoising by means of a deep
convolutional network. Our results show that gSUPPOSe can address the
localization of multiple overlapping emitters even at a low number of
acquired photons, outperforming CS-STORM in our quantitative analysis
and having better computational times. We also demonstrate that image
denoising greatly improves CS-STORM, showing the potential of deep
learning enhanced localization on existing SMLM algorithms. The
software developed in this work is available as open source Python
libraries.
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