2021 Digital Image Computing: Techniques and Applications (DICTA) 2021
DOI: 10.1109/dicta52665.2021.9647283
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Three-dimensional tumour microenvironment reconstruction and tumour-immune interactions' analysis

Abstract: Tumours arise within complex 3D microenvironments, but the routine 2D analysis of tumours often underestimates the spatial heterogeneity. In this paper, we present a methodology to reconstruct and analyse 3D tumour models from routine clinical samples allowing 3D interactions to be analysed at cellular resolution. Our workflow involves cutting thin serial sections of tumours followed by labelling of cells using markers of interest. Serial sections are then scanned, and digital multiplexed data are created for … Show more

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Cited by 4 publications
(3 citation statements)
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“…Given the various challenges, several techniques and methods have been developed, based on either hand-crafted or deep learning features. Hand-crafted developed approaches are based on grayscale density, color, texture and shape information extracting low-level or mid-level set of features [8][9][10]. On the other hand, more sophisticated methods [11,12] and deep-learning techniques including convolutional neural networks (CNNs) [13] and visual transformers (VTs) [14,15] have been developed aiming to address medical image challenges by extracting high-level features directly from the data.…”
Section: Related Workmentioning
confidence: 99%
“…Given the various challenges, several techniques and methods have been developed, based on either hand-crafted or deep learning features. Hand-crafted developed approaches are based on grayscale density, color, texture and shape information extracting low-level or mid-level set of features [8][9][10]. On the other hand, more sophisticated methods [11,12] and deep-learning techniques including convolutional neural networks (CNNs) [13] and visual transformers (VTs) [14,15] have been developed aiming to address medical image challenges by extracting high-level features directly from the data.…”
Section: Related Workmentioning
confidence: 99%
“…Thus, various techniques and methods, based on either hand-crafted or deep learning features, have been developed for histopathological image classification tasks. Hand-crafted developed classification approaches for digital pathology tasks are based on grayscale density, color, texture and shape information [9], [10], [11]. After the extraction of low-level or mid-level set of features, post-processing methods such as dimensionality reduction and a classifier are usually used aiming to assign a classification label to each image [12].…”
Section: Related Workmentioning
confidence: 99%
“…The digital medical image segmentation and classification field receives growing attention and has become more and more popular [10]. Thus, various techniques and methods, based on either hand-crafted or deep learning features, have been developed for histopathological image segmentation and classification tasks.…”
Section: Related Workmentioning
confidence: 99%