Yao-Jen Chang

Estimating a Patient Surface Model for Optimizing the Medical Scanning Workflow

In this paper, we present the idea of equipping a tomographic medical scanner with a range imaging device (e.g. a 3D camera) to improve the current scanning workflow. A novel technical approach is proposed to robustly estimate patient surface geometry by a single snapshot from the camera. Leveraging the information of the patient surface geometry can provide significant clinical benefits, including automation of the scan, motion compensation for better image quality, sanity check of patient movement, augmented reality for guidance, patient specific dose optimization, and more. Our approach overcomes the technical difficulties resulting from suboptimal camera placement due to practical considerations. Experimental results on more than 30 patients from a real CT scanner demonstrate the robustness of our approach.

DARWIN: Deformable Patient Avatar Representation With Deep Image Network

In this paper, we present a technical approach to robustly estimate the detailed patient body surface mesh under clothing cover from a single snapshot of a range sensor. Existing methods either lack level of detail of the estimated patient body model, fail to estimate the body model robustly under clothing cover, or lack sufficient evaluation over real patient datasets. In this work, we overcome these limitations by learning deep convolutional networks over real clinical dataset with large variation and augmentation. Our approach is validated with experiments conducted over 1063 human subjects from 3 different hospitals and surface errors are measured against groundtruth from CT data.

Multimodal Image Registration with Deep Context Reinforcement Learning

Automatic and robust registration between real-time patient imaging and pre-operative data (e.g. CT and MRI) is crucial for computer-aided interventions and AR-based navigation guidance. In this paper, we present a novel approach to automatically align range image of the patient with pre-operative CT images. Unlike existing approaches based on the surface similarity optimization process, our algorithm leverages the contextual information of medical images to resolve data ambiguities and improve robustness. The proposed algorithm is derived from deep reinforcement learning algorithm that automatically learns to extract optimal feature representation to reduce the appearance discrepancy between these two modalities. Quantitative evaluations on 1788 pairs of CT and depth images from real clinical setting demonstrate that the proposed method achieves the state-of-the-art performance.