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The standard medical practice for cancer diagnosis requires histopathology, which is an invasive and time-consuming procedure. Optical coherence tomography (OCT) is an alternative that is relatively fast, noninvasive, and able to capture three-dimensional structures of epithelial tissue. Unlike most previous OCT systems, which cannot capture crucial cellular-level information for squamous cell carcinoma (SCC) diagnosis, the full-field OCT (FF-OCT) technology used in this paper is able to produce images at sub-micron resolution and thereby facilitates the development of a deep learning algorithm for SCC detection. Experimental results show that the SCC detection algorithm can achieve a classification accuracy of 80% for mouse skin. Using the submicron FF-OCT imaging system, the proposed SCC detection algorithm has the potential for in-vivo applications.

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Accurate alignment of multi-session medical imaging is essential to the analysis of disease progression. By comparing the magnetic resonance imaging (MRI) data captured before and after a course of neoadjuvant chemoradiation (nCRT) treatment, physicians are able to evaluate the tumor response for further treatment of the disease. However, rectal MRI data captured in multi-session are often misaligned and not guaranteed to have one-to-one correspondence, making it challenging for physicians to observe the treatment response of tumor. To address this issue, we propose an unsupervised learning based volume registration framework, which enables accurate alignment even under a high degree of deformation between multi-session rectal data. Moreover, it works without the assumption of one-to-one correspondence between multi-session data, and hence is a general solution to rectal MRI volume registration. The experimental results show that the proposed registration framework accurately aligns rectal cancer images and outperforms other state-of-the-art methods in medical image registration. By providing accurate registration, it can potentially increase the efficiency and reduce the workload for physicians to evaluate the rectal tumor response to nCRT.

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Computational Imaging proceedings.