Search results for "Tumor region"
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SuperHistopath: A Deep Learning Pipeline for Mapping Tumor Heterogeneity on Low-Resolution Whole-Slide Digital Histopathology Images.
2021
High computational cost associated with digital pathology image analysis approaches is a challenge towards their translation in routine pathology clinic. Here, we propose a computationally efficient framework (SuperHistopath), designed to map global context features reflecting the rich tumor morphological heterogeneity. SuperHistopath efficiently combines i) a segmentation approach using the linear iterative clustering (SLIC) superpixels algorithm applied directly on the whole-slide images at low resolution (5x magnification) to adhere to region boundaries and form homogeneous spatial units at tissue-level, followed by ii) classification of superpixels using a convolution neural network (CN…
Differentiation between Brain Metastasis and Glioblastoma using MRI and two-dimensional Turbo Spectroscopic Imaging data
2009
In this paper we propose a novel technique to differentiate brain metastases from high-grade gliomas, which represent the most aggressive and common brain lesions. In spite of the significant progresses achieved in the field of MRI in the last decades, the differentiation between these two types of tumors is still a challenge as they show a similar appearance on MRI images, but require a completely different therapeutic treatment. Here, we show that such a differentiation is actually possible and can be obtained by making use of MRI as well as of two-dimensional Turbo Spectroscopic Imaging (2D-TSI) information. Specifically, the proposed technique consists of three steps: we first detect th…