The development of effective algorithms for removing surgical smoke in laparoscopic surgery has been hindered by the absence of a paired dataset containing real smoky and smoke-free surgical scenes. As a result, existing de-smoking methods have been primarily based on synthetic datasets and non-reference image enhancement metrics, which fail to fully capture the complexity of in vivo surgical scenes. To address this gap, we present a novel paired dataset derived from laparoscopic surgical recordings by identifying video sequences with relatively stationary scenes where smoke emerges. Our approach includes a robust motion-tracking technique that compensates for involuntary patient movements, ensuring reliable pairing of smoky images and their corresponding smoke-free ground truths. From 132 laparoscopic prostatectomy recordings, we curated 41 video sequences, resulting in a dataset of 2000 smoky-to-smoke-free image pairs. From 45 cholecystectomy recordings, we extracted 68 video sequences, resulting in an additional dataset of 1000 image pairs. Using this unique dataset, we evaluated a representative selection of current de-smoking methods, confirming their effectiveness while also highlighting their limitations. Furthermore, we critically revisited the commonly used atmospheric scattering model, atmospheric colour assumptions, and the dark channel prior. Our analysis demonstrated that the traditional atmospheric scattering model with "gray smoke" assumption introduces significant residual errors in the green and blue channels, while the dark channel prior maintains a strong correlation with smoke intensity. These observations suggest that, while less effective for direct smoke separation, the dark channel prior has potential to serve as a useful attention map for deep learning-based de-smoking approaches.

There is an increasing interest in the applications of 3D ultrasound imaging of the pelvic floor to improve the diagnosis, treatment, and surgical planning of female pelvic floor dysfunction (PFD). Pelvic floor biometrics are obtained on an oblique image plane known as the plane of minimal hiatal dimensions (PMHD). Identifying this plane requires the detection of two anatomical landmarks, the pubic symphysis and anorectal angle. The manual detection of the anatomical landmarks and the PMHD in 3D pelvic ultrasound requires expert knowledge of the pelvic floor anatomy, and is challenging, time-consuming, and subject to human error. These challenges have hindered the adoption of such quantitative analysis in the clinic. This work presents an automatic approach to identify the anatomical landmarks and extract the PMHD from 3D pelvic ultrasound volumes. To demonstrate clinical utility and a complete automated clinical task, an automatic segmentation of the levator-ani muscle on the extracted PMHD images was also performed. Experiments using 73 test images of patients during a pelvic muscle resting state showed that this algorithm has the capability to accurately identify the PMHD with an average Dice of 0.89 and an average mean boundary distance of 2.25mm. Further evaluation of the PMHD detection algorithm using 35 images of patients performing pelvic muscle contraction resulted in an average Dice of 0.88 and an average mean boundary distance of 2.75mm. This work had the potential to pave the way towards the adoption of ultrasound in the clinic and development of personalized treatment for PFD.

Stereo matching has become an active area of research in the field of computer vision. In minimally invasive surgery, stereo matching provides depth information to surgeons, with the potential to increase the safety of surgical procedures, particularly those performed laparoscopically. Many stereo matching methods have been reported to perform well for natural images, but for images acquired during a laparoscopic procedure, they are limited by image characteristics including illumination differences, weak texture content, specular highlights, and occlusions. To overcome these limitations, we propose a robust edge-preserving stereo matching method for laparoscopic images, comprising an efficient sparse-dense feature matching step, left and right image illumination equalization, and refined disparity optimization. We validated the proposed method using both benchmark biological phantoms and surgical stereoscopic data. Experimental results illustrated that, in the presence of heavy illumination differences between image pairs, texture and textureless surfaces, specular highlights and occlusions, our proposed approach consistently obtains a more accurate estimate of the disparity map than state-of-the-art stereo matching methods in terms of robustness and boundary preservation.

Complete tumor coverage by the thermal ablation zone and with a safety margin (5 or 10 mm) is required to achieve the entire tumor eradication in liver tumor ablation procedures. However, 2D ultrasound (US) imaging has limitations in evaluating the tumor coverage by imaging only one or multiple planes, particularly for cases with multiple inserted applicators or irregular tumor shapes. In this paper, we evaluate the intra-procedural tumor coverage using 3D US imaging and investigate whether it can provide clinically needed information. Using data from 14 cases, we employed surface- and volume-based evaluation metrics to provide information on any uncovered tumor region. For cases with incomplete tumor coverage or uneven ablation margin distribution, we also proposed a novel margin uniformity -based approach to provide quantitative applicator adjustment information for optimization of tumor coverage. Both the surface- and volume-based metrics showed that 5 of 14 cases had incomplete tumor coverage according to the estimated ablation zone. After applying our proposed applicator adjustment approach, the simulated results showed that 92.9% (13 of 14) cases achieved 100% tumor coverage and the remaining case can benefit by increasing the ablation time or power. Our proposed method can evaluate the intra-procedural tumor coverage and intuitively provide applicator adjustment information for the physician. Our 3D US-based method is compatible with the constraints of conventional US-guided ablation procedures and can be easily integrated into the clinical workflow.

In situ visualization of laparoscopic ultrasound in both conventional and robot-assisted laparoscopic surgery requires robust and efficient computation of the pose of the laparoscopic ultrasound probe with respect to the laparoscopic camera. Image-based intrinsic methods of computing this relative pose need to overcome challenges due to irregular illumination, partial feature occlusion, and clutter that are unavoidable in practical laparoscopic surgery. In this paper, we propose an accurate image-based method that is robust to partial occlusion of the fiducials and outliers. The method is extended to multi-view imaging model with applications in stereoscopic laparoscopy and robot-assisted surgery. Rather than treating the model-to-image correspondence and pose computation as separate problems, we solve them jointly using the Kalman Filter-based framework that demonstrates video rate running time (~24fps). By keeping the optical tracking measurements as a reference, we demonstrate that the proposed methods result in clinically acceptable tracking accuracy, reaching target registration errors well below 1.5mm on average. In addition, our multi-view tracking method is compared to a conventional stereo triangulation-based pose estimation scheme that commercial optical tracking systems are based on, to experimentally demonstrate its superiority in terms of accuracy. Finally, we qualitatively demonstrate the suitability of our methods for practical laparoscopic applications by conducting a phantom-based experiment.