Patients diagnosed with early stage (Stage I/II) Oral Cavity Cancer (OCC) are typically treated with surgery alone. Unfortunately, 25-37% of early stage OCC patients experience loco-regional tumor recurrence after receiving surgery. Currently, pathologists use the Histologic Risk Model (HRM), a clinically validated risk assessment tool to determine patient prognosis. In this study, we perform image registration on two cases of serially sectioned blocks of Hematoxylin and Eosin (H and E) stained OCC tissue sections. The goal of this work is to create an optimized registration procedure to reconstruct 3D tissue models, which can provide a pathologist with a realistic representation of the tissue architecture before surgical resection. Our project aims to extend the HRM to enhance prediction performance for patients at high risk of disease progression using computational pathology tools. In previous literature, others have explored image registration of histological slides and reconstructing 3D models with similar processes used. Our work is unique in that we are investigating in-depth the parameter space of an image registration algorithm to establish a registration procedure for any serial histological section. Each parameter set was sequentially perturbed to determine the best parameter set for registration, as evaluated through mutual information.
Starr Johnson and Margaret Brandwein and Scott Doyle, “Registration parameter optimization for 3D tissue modeling from resected tumors cut into serial H&E slides,” Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810T (2018).
I am Assistant Professor of Pathology and Anatomical Sciences, Biomedical Engineering, and Biomedical Informatics at the University at Buffalo, SUNY. My lab develops computational tools for medical data, with a focus on imaging, machine learning, and artificial intelligence.