Localizing oneself during endoscopic procedures can be problematic due to the
lack of distinguishable textures and landmarks, as well as difficulties due to
the endoscopic device such as a limited field of view and challenging lighting
conditions. Expert knowledge shaped by years of experience is required for
localization within the human body during endoscopic procedures. In this work,
we present a deep learning method based on anatomy recognition, that constructs
a surgical path in an unsupervised manner from surgical videos, modelling
relative location and variations due to different viewing angles. At inference
time, the model can map an unseen video's frames on the path and estimate the
viewing angle, aiming to provide guidance, for instance, to reach a particular
destination. We test the method on a dataset consisting of surgical videos of
transsphenoidal adenomectomies, as well as on a synthetic dataset. An online
tool that lets researchers upload their surgical videos to obtain anatomy
detections and the weights of the trained YOLOv7 model are available at:
this https URL

基于解剖识别的深度学习方法，通过无监督学习从手术视频中构建手术路径，从而解决内窥镜手术中由于缺乏可区分的纹理和地标以及内窥镜设备的局限性（如有限的视野和光照条件）所带来的定位问题。

基于视觉的神经外科导航：无监督定位和相机姿态预测

Vision-Based Neurosurgical Guidance: Unsupervised Localization and  Camera-Pose Prediction

Tool tracking in surgical videos is vital in computer-assisted intervention
for tasks like surgeon skill assessment, safety zone estimation, and
human-machine collaboration during minimally invasive procedures. The lack of
large-scale datasets hampers Artificial Intelligence implementation in this
domain. Current datasets exhibit overly generic tracking formalization, often
lacking surgical context: a deficiency that becomes evident when tools move out
of the camera's scope, resulting in rigid trajectories that hinder realistic
surgical representation. This paper addresses the need for a more precise and
adaptable tracking formalization tailored to the intricacies of endoscopic
procedures by introducing CholecTrack20, an extensive dataset meticulously
annotated for multi-class multi-tool tracking across three perspectives
representing the various ways of considering the temporal duration of a tool
trajectory: (1) intraoperative, (2) intracorporeal, and (3) visibility within
the camera's scope. The dataset comprises 20 laparoscopic videos with over
35,000 frames and 65,000 annotated tool instances with details on spatial
location, category, identity, operator, phase, and surgical visual conditions.
This detailed dataset caters to the evolving assistive requirements within a
procedure.

通过引入 CholecTrack20 数据集，本文解决了在外科视频中进行工具跟踪的需求，该数据集是根据内窥镜手术的复杂性精心注释的，可跟踪多类多工具的三个不同视角的工具轨迹，包括内窥镜手术、人体内部手术和相机视野中的工具可见性，该详细数据集满足了手术过程中不断变化的辅助要求。