Background and Objective: In neurosurgery, fusing clinical images and depth
images that can improve the information and details is beneficial to surgery.
We found that the registration of face depth images was invalid frequently
using existing methods. To abundant traditional image methods with depth
information, a method in registering with depth images and traditional clinical
images was investigated. Methods: We used the dlib library, a C++ library that
could be used in face recognition, and recognized the key points on faces from
the structure light camera and CT image. The two key point clouds were
registered for coarse registration by the ICP method. Fine registration was
finished after coarse registration by the ICP method. Results: RMSE after
coarse and fine registration is as low as 0.995913 mm. Compared with
traditional methods, it also takes less time. Conclusions: The new method
successfully registered the facial depth image from structure light images and
CT with a low error, and that would be promising and efficient in clinical
application of neurosurgery.

神经外科的一项新方法成功地将结构光图像与 CT 扫描中的颜面深度图像进行了注册，误差很低，有希望在神经外科的临床应用中发挥有效作用。

基于结构光图像的临床面部登记新方法

A New Method in Facial Registration in Clinics Based on Structure Light  Images

Surgery for brain cancer is a major problem in neurosurgery. The diffuse
infiltration into the surrounding normal brain by these tumors makes their
accurate identification by the naked eye difficult. Since surgery is the common
treatment for brain cancer, an accurate radical resection of the tumor leads to
improved survival rates for patients. However, the identification of the tumor
boundaries during surgery is challenging. Hyperspectral imaging is a
noncontact, non-ionizing and non-invasive technique suitable for medical
diagnosis. This study presents the development of a novel classification method
taking into account the spatial and spectral characteristics of the
hyperspectral images to help neurosurgeons to accurately determine the tumor
boundaries in surgical-time during the resection, avoiding excessive excision
of normal tissue or unintentionally leaving residual tumor. The algorithm
proposed in this study to approach an efficient solution consists of a hybrid
framework that combines both supervised and unsupervised machine learning
methods. To evaluate the proposed approach, five hyperspectral images of
surface of the brain affected by glioblastoma tumor in vivo from five different
patients have been used. The final classification maps obtained have been
analyzed and validated by specialists. These preliminary results are promising,
obtaining an accurate delineation of the tumor area.

开发了一种考虑到高光谱图像的空间和光谱特征的新型分类方法，帮助神经外科医生在手术过程中准确确定肿瘤边界，避免过度切除正常组织或意外剩余肿瘤，初步结果有望获得肿瘤区域的准确划分。

手术过程中基于高光谱图像的脑癌检测的时空谱分类

Spatio-spectral classification of hyperspectral images for brain cancer  detection during surgical operations

Resting State Networks (RSNs) of the brain extracted from Resting State
functional Magnetic Resonance Imaging (RS-fMRI) are used in the pre-surgical
planning to guide the neurosurgeon. This is difficult, though, as expert
knowledge is required to label each of the RSNs. There is a lack of efficient
and standardized methods to be used in clinical workflows. Additionally, these
methods need to be generalizable since the method needs to work well regardless
of the acquisition technique. We propose an accurate, fast, and lightweight
deep learning approach to label RSNs. Group Independent Component Analysis
(ICA) was used to extract large scale functional connectivity patterns in the
cohort and dual regression was used to back project them on individual subject
RSNs. We compare a Multi-Layer Perceptron (MLP) based method with 2D and 3D
Convolutional Neural Networks (CNNs) and find that the MLP is faster and more
accurate. The MLP method performs as good or better than other works despite
its compact size. We prove the generalizability of our method by showing that
the MLP performs at 100% accuracy in the holdout dataset and 98.3% accuracy in
three other sites' fMRI acquisitions.

研究使用深度学习方法快速准确地标记静息态功能磁共振成像中的脑区网络，通过测试其在不同数据集和磁共振成像获取技术下的表现，证明其通用性和可靠性。