Hao Li

Hao Li, Daiwei Lu, Jesse d'Almeida et al.

Monocular depth estimation (MDE) is a critical task to guide autonomous medical robots. However, obtaining absolute (metric) depth from an endoscopy camera in surgical scenes is difficult, which limits supervised learning of depth on real endoscopic images. Current image-level unsupervised domain adaptation methods translate synthetic images with known depth maps into the style of real endoscopic frames and train depth networks using these translated images with their corresponding depth maps. However a domain gap often remains between real and translated synthetic images. In this paper, we present a latent feature alignment method to improve absolute depth estimation by reducing this domain gap in the context of endoscopic videos of the central airway. Our methods are agnostic to the image translation process and focus on the depth estimation itself. Specifically, the depth network takes translated synthetic and real endoscopic frames as input and learns latent domain-invariant features via adversarial learning and directional feature consistency. The evaluation is conducted on endoscopic videos of central airway phantoms with manually aligned absolute depth maps. Compared to state-of-the-art MDE methods, our approach achieves superior performance on both absolute and relative depth metrics, and consistently improves results across various backbones and pretrained weights. Our code is available at https://github.com/MedICL-VU/MDE.

Hao Li, Baris Oguz, Gabriel Arenas et al.

Placenta volume measured from 3D ultrasound (3DUS) images is an important tool for tracking the growth trajectory and is associated with pregnancy outcomes. Manual segmentation is the gold standard, but it is time-consuming and subjective. Although fully automated deep learning algorithms perform well, they do not always yield high-quality results for each case. Interactive segmentation models could address this issue. However, there is limited work on interactive segmentation models for the placenta. Despite their segmentation accuracy, these methods may not be feasible for clinical use as they require relatively large computational power which may be especially prohibitive in low-resource environments, or on mobile devices. In this paper, we propose a lightweight interactive segmentation model aiming for clinical use to interactively segment the placenta from 3DUS images in real-time. The proposed model adopts the segmentation from our fully automated model for initialization and is designed in a human-in-the-loop manner to achieve iterative improvements. The Dice score and normalized surface Dice are used as evaluation metrics. The results show that our model can achieve superior performance in segmentation compared to state-of-the-art models while using significantly fewer parameters. Additionally, the proposed model is much faster for inference and robust to poor initial masks. The code is available at https://github.com/MedICL-VU/PRISM-placenta.

Ayberk Acar, Mariana Smith, Lidia Al-Zogbi et al.

Surgical automation requires precise guidance and understanding of the scene. Current methods in the literature rely on bulky depth cameras to create maps of the anatomy, however this does not translate well to space-limited clinical applications. Monocular cameras are small and allow minimally invasive surgeries in tight spaces but additional processing is required to generate 3D scene understanding. We propose a 3D mapping pipeline that uses only RGB images to create segmented point clouds of the target anatomy. To ensure the most precise reconstruction, we compare different structure from motion algorithms' performance on mapping the central airway obstructions, and test the pipeline on a downstream task of tumor resection. In several metrics, including post-procedure tissue model evaluation, our pipeline performs comparably to RGB-D cameras and, in some cases, even surpasses their performance. These promising results demonstrate that automation guidance can be achieved in minimally invasive procedures with monocular cameras. This study is a step toward the complete autonomy of surgical robots.

Ayberk Acar, Fangjie Li, Hao Li et al.

Central airway obstruction (CAO) is a life-threatening condition with increasing incidence, caused by tumors in and outside of the airway. Traditional treatment methods such as bronchoscopy and electrocautery can be used to remove the tumor completely; however, these methods carry a high risk of complications. Recent advances allow robotic interventions with lesser risk. The combination of robot interventions with scene understanding and mapping also opens up the possibilities for automation. We present a novel pipeline that enables real-time, semantically informed 3D reconstructions of the central airway using monocular endoscopic video. Our approach combines DROID-SLAM with a segmentation model trained to identify obstructive tissues. The SLAM module reconstructs the 3D geometry of the airway in real time, while the segmentation masks guide the annotation of obstruction regions within the reconstructed point cloud. To validate our pipeline, we evaluate the reconstruction quality using ex vivo models. Qualitative and quantitative results show high similarity between ground truth CT scans and the 3D reconstructions (0.62 mm Chamfer distance). By integrating segmentation directly into the SLAM workflow, our system produces annotated 3D maps that highlight clinically relevant regions in real time. High-speed capabilities of the pipeline allows quicker reconstructions compared to previous work, reflecting the surgical scene more accurately. To the best of our knowledge, this is the first work to integrate semantic segmentation with real-time monocular SLAM for endoscopic CAO scenarios. Our framework is modular and can generalize to other anatomies or procedures with minimal changes, offering a promising step toward autonomous robotic interventions.