Colorectal cancer screening through colonoscopy continues to be the dominant
global standard, as it allows identifying pre-cancerous or adenomatous lesions
and provides the ability to remove them during the procedure itself.
Nevertheless, failure by the endoscopist to identify such lesions increases the
likelihood of lesion progression to subsequent colorectal cancer. Ultimately,
colonoscopy remains operator-dependent, and the wide range of quality in
colonoscopy examinations among endoscopists is influenced by variations in
their technique, training, and diligence. This paper presents a novel real-time
navigation guidance system for Optical Colonoscopy (OC). Our proposed system
employs a real-time approach that displays both an unfolded representation of
the colon and a local indicator directing to un-inspected areas. These
visualizations are presented to the physician during the procedure, providing
actionable and comprehensible guidance to un-surveyed areas in real-time, while
seamlessly integrating into the physician's workflow. Through coverage
experimental evaluation, we demonstrated that our system resulted in a higher
polyp recall (PR) and high inter-rater reliability with physicians for coverage
prediction. These results suggest that our real-time navigation guidance system
has the potential to improve the quality and effectiveness of Optical
Colonoscopy and ultimately benefit patient outcomes.

该论文介绍了一种基于实时导航系统的光学结肠镜检查技术，通过在操作过程中提供定位信息，从而提高了肿块检测率和医生的预测成功率。

ColNav: 结肠镜实时导航系统

ColNav: Real-Time Colon Navigation for Colonoscopy

3D colon reconstruction from Optical Colonoscopy (OC) to detect non-examined
surfaces remains an unsolved problem. The challenges arise from the nature of
optical colonoscopy data, characterized by highly reflective low-texture
surfaces, drastic illumination changes and frequent tracking loss. Recent
methods demonstrate compelling results, but suffer from: (1) frangible
frame-to-frame (or frame-to-model) pose estimation resulting in many tracking
failures; or (2) rely on point-based representations at the cost of scan
quality. In this paper, we propose a novel reconstruction framework that
addresses these issues end to end, which result in both quantitatively and
qualitatively accurate and robust 3D colon reconstruction. Our SLAM approach,
which employs correspondences based on contrastive deep features, and deep
consistent depth maps, estimates globally optimized poses, is able to recover
from frequent tracking failures, and estimates a global consistent 3D model;
all within a single framework. We perform an extensive experimental evaluation
on multiple synthetic and real colonoscopy videos, showing high-quality results
and comparisons against relevant baselines.

本文提出了一种新颖的重建框架，采用对比深度特征和深度一致性深度图的对应方法，通过单一框架实现全局优化姿势，能够从频繁的跟踪失败中恢复，并估计全局一致的三维模型，从而实现了量化和质量上准确而健壮的三维结肠重建。

C$^3$Fusion：一致性对比结肠融合，迈向深度结肠镜 SLAM

C$^3$Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy

Colorectal cancer screening modalities, such as optical colonoscopy (OC) and
virtual colonoscopy (VC), are critical for diagnosing and ultimately removing
polyps (precursors of colon cancer). The non-invasive VC is normally used to
inspect a 3D reconstructed colon (from CT scans) for polyps and if found, the
OC procedure is performed to physically traverse the colon via endoscope and
remove these polyps. In this paper, we present a deep learning framework,
Extended and Directional CycleGAN, for lossy unpaired image-to-image
translation between OC and VC to augment OC video sequences with
scale-consistent depth information from VC, and augment VC with
patient-specific textures, color and specular highlights from OC (e.g, for
realistic polyp synthesis). Both OC and VC contain structural information, but
it is obscured in OC by additional patient-specific texture and specular
highlights, hence making the translation from OC to VC lossy. The existing
CycleGAN approaches do not handle lossy transformations. To address this
shortcoming, we introduce an extended cycle consistency loss, which compares
the geometric structures from OC in the VC domain. This loss removes the need
for the CycleGAN to embed OC information in the VC domain. To handle a stronger
removal of the textures and lighting, a Directional Discriminator is introduced
to differentiate the direction of translation (by creating paired information
for the discriminator), as opposed to the standard CycleGAN which is
direction-agnostic. Combining the extended cycle consistency loss and the
Directional Discriminator, we show state-of-the-art results on scale-consistent
depth inference for phantom, textured VC and for real polyp and normal colon
video sequences. We also present results for realistic pendunculated and flat
polyp synthesis from bumps introduced in 3D VC models. Code/models:
this https URL

使用深度学习框架，本研究提出了一种新的方法来实现 OC 和 VC 之间的图像 - 图像转换，并演示了在虚拟结肠镜检查，真实息肉和正常结肠视频序列以及 3D VC 模型中创造逼真的息肉的结果。