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Most current deep learning models for hematoxylin and eosin (H&E) histopathology image analysis lack the power of generalization to datasets collected from other institutes due to the domain shift in the data. In this research, we study the domain shift problem on two prostate cancer (PCa) datasets collected from the Vancouver Prostate Centre (source dataset) and the University of Colorado (target dataset) and develop a novel centerbased H&E color augmentation for cross-center model generalization. While previous work used methods such as random augmentation, color normalization, or learning domain-independent features to improve the robustness of the model to changes in H&E stains, our method first augments the H&E color space of the source dataset to color space of both datasets and then adds random color augmentation. Our method covers the larger range of the color distribution of both institutions resulting in a better generalization. We compared our method with two different State-Of-The-Art (SOTA) un-annotated domain adaptation methods: color normalization and unsupervised domain adversarial neural network (DANN) training, with an ablation study. Our proposed method improves the model performance on both the source and target datasets, and has the best performance on the unlabeled target dataset, showing promise as an approach to learning more generalizable features for histopathology image analysis.

Trans-Oral Robotic Surgery (TORS) is an alternative surgery technique used to treat head-and-neck cancer. Compared with conventional surgery, robot assistance allows surgeons to operate within areas with restricted access, such as the oropharynx, reducing the operative morbidity, risk of reconstructive surgery and improving patient outcomes. TORS is a challenging procedure, and intra-operative Ultrasound (US) has the potential to improve anatomy visualization to lessen the cognitive load on surgeons. To date, only intra-oral US has been used in exploratory studies, but intra-oral US can interfere with robot tools. In this study, we assess the feasibility of using transcervical 3D US with TORS: we propose to place the US probe on the patient’s neck to evaluate oropharyngeal anatomy intra-operatively. We also perform the first feasibility study of image registration between transcervical 3D US and Magnetic Resonance Imaging (MRI) for the oropharynx. We collected 3D US and MRI data from five healthy volunteers and four patients with oropharyngeal cancer, and we use a semi-automatic MRI-US registration algorithm to estimate an affine transformation between the two image spaces. The average Target Registration Error (TRE) is 8.26 ± 7.41mm for healthy volunteers and 9.63 ± 5.91mm for patients, and our case studies show that image quality is the key factor for good registration. Our work shows that 3D transcervical US has the clinical potential to enable intraoperative oropharynx imaging and interventional MR guidance during TORS.