Augmented Reality (AR) has been used to facilitate surgical guidance during
External Ventricular Drain (EVD) surgery, reducing the risks of misplacement in
manual operations. During this procedure, the pivotal challenge is the accurate
estimation of spatial relationship between pre-operative images and actual
patient anatomy in AR environment. In this research, we propose a novel
framework utilizing Time of Flight (ToF) depth sensors integrated in
commercially available AR Head Mounted Devices (HMD) for precise EVD surgical
guidance. As previous studies have proven depth errors for ToF sensors, we
first conducted a comprehensive assessment for the properties of this error on
AR-HMDs. Subsequently, a depth error model and patient-specific model parameter
identification method, is introduced for accurate surface information. After
that, a tracking procedure combining retro-reflective markers and point clouds
is proposed for accurate head tracking, where head surface is reconstructed
using ToF sensor data for spatial registration, avoiding fixing tracking
targets rigidly on the patient's cranium. Firstly, $7.580\pm 1.488 mm$ ToF
sensor depth value error was revealed on human skin, indicating the
significance of depth correction. Our results showed that the ToF sensor depth
error was reduced by over $85\%$ using proposed depth correction method on head
phantoms in different materials. Meanwhile, the head surface reconstructed with
corrected depth data achieved sub-millimeter accuracy. Experiment on a sheep
head revealed $0.79 mm$ reconstruction error. Furthermore, a user study was
conducted for the performance of proposed framework in simulated EVD surgery,
where 5 surgeons performed 9 k-wire injections on a head phantom with virtual
guidance. Results of this study revealed $2.09 \pm 0.16 mm$ translational
accuracy and $2.97\pm 0.91 ^\circ$ orientational accuracy.

该研究提出了一种采用 ToF 深度传感器和反射标记的 AR 头戴式设备的新框架，可以实现高精度的 EVD 手术导航。通过深度校正和面部表面重建，可以大幅减少深度误差并实现亚毫米级精度，同时在仿真的 EVD 手术中实现了较高的定位精度。

利用头戴式增强现实设备进行后向反射工具追踪和空间重建的埃博拉病毒疾病手术指导

EVD Surgical Guidance with Retro-Reflective Tool Tracking and Spatial  Reconstruction using Head-Mounted Augmented Reality Device

Tracking humans in crowded video sequences is an important constituent of
visual scene understanding. Increasing crowd density challenges visibility of
humans, limiting the scalability of existing pedestrian trackers to higher
crowd densities. For that reason, we propose to revitalize head tracking with
Crowd of Heads Dataset (CroHD), consisting of 9 sequences of 11,463 frames with
over 2,276,838 heads and 5,230 tracks annotated in diverse scenes. For
evaluation, we proposed a new metric, IDEucl, to measure an algorithm's
efficacy in preserving a unique identity for the longest stretch in image
coordinate space, thus building a correspondence between pedestrian crowd
motion and the performance of a tracking algorithm. Moreover, we also propose a
new head detector, HeadHunter, which is designed for small head detection in
crowded scenes. We extend HeadHunter with a Particle Filter and a color
histogram based re-identification module for head tracking. To establish this
as a strong baseline, we compare our tracker with existing state-of-the-art
pedestrian trackers on CroHD and demonstrate superiority, especially in
identity preserving tracking metrics. With a light-weight head detector and a
tracker which is efficient at identity preservation, we believe our
contributions will serve useful in advancement of pedestrian tracking in dense
crowds.

该研究提出一种基于头部检测和轨迹跟踪的算法来解决人群密度大的环境下的行人追踪问题，并提出了一种新的衡量算法效果的指标，并在 Crowd of Heads Dataset（CroHD）上进行了比较，表现优异。