This paper presents a novel method of smoke removal from the laparoscopic
images. Due to the heterogeneous nature of surgical smoke, a two-stage network
is proposed to estimate the smoke distribution and reconstruct a clear,
smoke-free surgical scene. The utilization of the lightness channel plays a
pivotal role in providing vital information pertaining to smoke density. The
reconstruction of smoke-free image is guided by a hybrid embedding, which
combines the estimated smoke mask with the initial image. Experimental results
demonstrate that the proposed method boasts a Peak Signal to Noise Ratio that
is $2.79\%$ higher than the state-of-the-art methods, while also exhibits a
remarkable $38.2\%$ reduction in run-time. Overall, the proposed method offers
comparable or even superior performance in terms of both smoke removal quality
and computational efficiency when compared to existing state-of-the-art
methods. This work will be publicly available on
this http URL

本文提出了一种新颖的方法，用于从腹腔镜图像中去除烟雾。该方法采用了二阶段网络来估计烟雾分布并重建清晰的无烟手术场景，光度通道的利用对提供烟雾密度的重要信息起着关键作用。实验结果表明，该方法在峰值信噪比方面比现有最先进的方法高出 2.79％，运行时间也减少了 38.2％。总体上，与现有最先进的方法相比，该方法在烟雾去除质量和计算效率方面具有可比甚至更优秀的性能。

基于注意力的腹腔镜图像去烟网络与亮度嵌入和混合引导嵌入

Attention-Aware Laparoscopic Image Desmoking Network with Lightness  Embedding and Hybrid Guided Embedding

Accurate identification and localization of anatomical structures of varying
size and appearance in laparoscopic imaging are necessary to leverage the
potential of computer vision techniques for surgical decision support.
Segmentation performance of such models is traditionally reported using metrics
of overlap such as IoU. However, imbalanced and unrealistic representation of
classes in the training data and suboptimal selection of reported metrics have
the potential to skew nominal segmentation performance and thereby ultimately
limit clinical translation. In this work, we systematically analyze the impact
of class characteristics (i.e., organ size differences), training and test data
composition (i.e., representation of positive and negative examples), and
modeling parameters (i.e., foreground-to-background class weight) on eight
segmentation metrics: accuracy, precision, recall, IoU, F1 score, specificity,
Hausdorff Distance, and Average Symmetric Surface Distance. Based on our
findings, we propose two simple yet effective strategies to improve real-world
applicability of image segmentation models in laparoscopic surgical data: (1)
inclusion of negative examples in the training process and (2) adaptation of
foreground-background weights in segmentation models to maximize model
performance with respect to specific metrics of interest, depending on the
clinical use case.

通过分析类别特征、训练和测试数据的组成以及建模参数对八个分割度量指标的影响，本研究提出了两种简单而有效的策略来改善腹腔镜手术数据中图像分割模型的实际适用性：（1）在训练过程中包含负样本，（2）调整分割模型的前景 - 背景权重以最大化与特定感兴趣度量相对应的模型性能，具体取决于临床用例。

提升腹腔镜解剖分割模型在实际应用中的适用性战略

Strategies to Improve Real-World Applicability of Laparoscopic Anatomy  Segmentation Models

Recent advances in synthetic imaging open up opportunities for obtaining
additional data in the field of surgical imaging. This data can provide
reliable supplements supporting surgical applications and decision-making
through computer vision. Particularly the field of image-guided surgery, such
as laparoscopic and robotic-assisted surgery, benefits strongly from synthetic
image datasets and virtual surgical training methods. Our study presents an
intuitive approach for generating synthetic laparoscopic images from short text
prompts using diffusion-based generative models. We demonstrate the usage of
state-of-the-art text-to-image architectures in the context of laparoscopic
imaging with regard to the surgical removal of the gallbladder as an example.
Results on fidelity and diversity demonstrate that diffusion-based models can
acquire knowledge about the style and semantics in the field of image-guided
surgery. A validation study with a human assessment survey underlines the
realistic nature of our synthetic data, as medical personnel detects actual
images in a pool with generated images causing a false-positive rate of 66%. In
addition, the investigation of a state-of-the-art machine learning model to
recognize surgical actions indicates enhanced results when trained with
additional generated images of up to 5.20%. Overall, the achieved image quality
contributes to the usage of computer-generated images in surgical applications
and enhances its path to maturity.

通过扩散生成模型，我们提出了一种直观的方法来从短文本提示生成合成的腹腔镜图像，采用最先进的文本到图像架构，在图像引导手术领域，特别是腹腔镜和机器人辅助手术方面，得到了依靠合成图像数据和虚拟手术训练方法的强力帮助。