Abstract
Mass spectrometry is an effective imaging tool for evaluating biological tissue to detect cancer. With the assistance of deep learning, this technology can be used as a perioperative tissue assessment tool that will facilitate informed surgical decisions. To achieve such a system requires the development of a database of mass spectrometry signals and their corresponding pathology labels. Assigning correct labels, in turn, necessitates precise spatial registration of histopathology and mass spectrometry data. This is a challenging task due to the domain differences and noisy nature of images. In this study, we create a registration framework for mass spectrometry and pathology images as a contribution to the development of perioperative tissue assessment. In doing so, we explore two opportunities in deep learning for medical image registration, namely, unsupervised, multi-modal deformable image registration and evaluation of the registration. We test this system on prostate needle biopsy cores that were imaged with desorption electrospray ionization mass spectrometry (DESI) and show that we can successfully register DESI and histology images to achieve accurate alignment and, consequently, labelling for future training. This automation is expected to improve the efficiency and development of a deep learning architecture that will benefit the use of mass spectrometry imaging for cancer diagnosis.
Highlights
Mass spectrometry imaging has gained attention in cancer research owing to its ability to capture the spatial distribution of tissue metabolites, mostly in the context of cancer pathology
The application of mass spectrometry imaging systems has the potential for perioperative tissue assessment to improve surgical outcomes
We extend existing approaches and DL frameworks to construct this network and propose new techniques for data augmentation and simulation as well as an iterative training approach. With this network in place, registration of Desorption electrospray ionization mass spectrometry imaging (DESI) and histology images can be performed efficiently and with minimal operator intervention. This should benefit the development of a DL algorithm for prostate cancer diagnosis with DESI imaging
Summary
Mass spectrometry imaging has gained attention in cancer research owing to its ability to capture the spatial distribution of tissue metabolites, mostly in the context of cancer pathology. Cancer biomarkers can be characterized in relation to tissue pathologies, as well as tumor aggressiveness using this technique [1]. The application of mass spectrometry imaging systems has the potential for perioperative tissue assessment to improve surgical outcomes. Desorption electrospray ionization mass spectrometry imaging (DESI). Is an example of a mass spectrometry imaging system that can be used in this application. DESI is an ambient ionization technique that can profile the metabolic signature of tissue samples at a resolution of 50 μm with minimal tissue preparation [1].
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