An Endoscopic Chisel: Intraoperative Imaging Carves 3D Anatomical Models

Purpose: Preoperative imaging plays a pivotal role in sinus surgery where CTs offer patient-specific insights of complex anatomy, enabling real-time intraoperative navigation to complement endoscopy imaging. However, surgery elicits anatomical changes not represented in the preoperative model, generating an inaccurate basis for navigation during surgery progression. Methods: We propose a first vision-based approach to update the preoperative 3D anatomical model leveraging intraoperative endoscopic video for navigated sinus surgery where relative camera poses are known. We rely on comparisons of intraoperative monocular depth estimates and preoperative depth renders to identify modified regions. The new depths are integrated in these regions through volumetric fusion in a truncated signed distance function representation to generate an intraoperative 3D model that reflects tissue manipulation. Results: We quantitatively evaluate our approach by sequentially updating models for a five-step surgical progression in an ex vivo specimen. We compute the error between correspondences from the updated model and ground-truth intraoperative CT in the region of anatomical modification. The resulting models show a decrease in error during surgical progression as opposed to increasing when no update is employed. Conclusion: Our findings suggest that preoperative 3D anatomical models can be updated using intraoperative endoscopy video in navigated sinus surgery. Future work will investigate improvements to monocular depth estimation as well as removing the need for external navigation systems. The resulting ability to continuously update the patient model may provide surgeons with a more precise understanding of the current anatomical state and paves the way toward a digital twin paradigm for sinus surgery.

A Quantitative Evaluation of Dense 3D Reconstruction of Sinus Anatomy from Monocular Endoscopic Video

Generating accurate 3D reconstructions from endoscopic video is a promising avenue for longitudinal radiation-free analysis of sinus anatomy and surgical outcomes. Several methods for monocular reconstruction have been proposed, yielding visually pleasant 3D anatomical structures by retrieving relative camera poses with structure-from-motion-type algorithms and fusion of monocular depth estimates. However, due to the complex properties of the underlying algorithms and endoscopic scenes, the reconstruction pipeline may perform poorly or fail unexpectedly. Further, acquiring medical data conveys additional challenges, presenting difficulties in quantitatively benchmarking these models, understanding failure cases, and identifying critical components that contribute to their precision. In this work, we perform a quantitative analysis of a self-supervised approach for sinus reconstruction using endoscopic sequences paired with optical tracking and high-resolution computed tomography acquired from nine ex-vivo specimens. Our results show that the generated reconstructions are in high agreement with the anatomy, yielding an average point-to-mesh error of 0.91 mm between reconstructions and CT segmentations. However, in a point-to-point matching scenario, relevant for endoscope tracking and navigation, we found average target registration errors of 6.58 mm. We identified that pose and depth estimation inaccuracies contribute equally to this error and that locally consistent sequences with shorter trajectories generate more accurate reconstructions. These results suggest that achieving global consistency between relative camera poses and estimated depths with the anatomy is essential. In doing so, we can ensure proper synergy between all components of the pipeline for improved reconstructions that will facilitate clinical application of this innovative technology.

AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation

Segmentation is a fundamental problem in surgical scene analysis using artificial intelligence. However, the inherent data scarcity in this domain makes it challenging to adapt traditional segmentation techniques for this task. To tackle this issue, current research employs pretrained models and finetunes them on the given data. Even so, these require training deep networks with millions of parameters every time new data becomes available. A recently published foundation model, Segment-Anything (SAM), generalizes well to a large variety of natural images, hence tackling this challenge to a reasonable extent. However, SAM does not generalize well to the medical domain as is without utilizing a large amount of compute resources for fine-tuning and using task-specific prompts. Moreover, these prompts are in the form of bounding-boxes or foreground/background points that need to be annotated explicitly for every image, making this solution increasingly tedious with higher data size. In this work, we propose AdaptiveSAM - an adaptive modification of SAM that can adjust to new datasets quickly and efficiently, while enabling text-prompted segmentation. For finetuning AdaptiveSAM, we propose an approach called bias-tuning that requires a significantly smaller number of trainable parameters than SAM (less than 2\%). At the same time, AdaptiveSAM requires negligible expert intervention since it uses free-form text as prompt and can segment the object of interest with just the label name as prompt. Our experiments show that AdaptiveSAM outperforms current state-of-the-art methods on various medical imaging datasets including surgery, ultrasound and X-ray. Code is available at https://github.com/JayParanjape/biastuning

Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos

Surgical tool presence detection is an important part of the intra-operative and post-operative analysis of a surgery. State-of-the-art models, which perform this task well on a particular dataset, however, perform poorly when tested on another dataset. This occurs due to a significant domain shift between the datasets resulting from the use of different tools, sensors, data resolution etc. In this paper, we highlight this domain shift in the commonly performed cataract surgery and propose a novel end-to-end Unsupervised Domain Adaptation (UDA) method called the Barlow Adaptor that addresses the problem of distribution shift without requiring any labels from another domain. In addition, we introduce a novel loss called the Barlow Feature Alignment Loss (BFAL) which aligns features across different domains while reducing redundancy and the need for higher batch sizes, thus improving cross-dataset performance. The use of BFAL is a novel approach to address the challenge of domain shift in cataract surgery data. Extensive experiments are conducted on two cataract surgery datasets and it is shown that the proposed method outperforms the state-of-the-art UDA methods by 6%. The code can be found at https://github.com/JayParanjape/Barlow-Adaptor

GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos

Automated surgical step recognition is an important task that can significantly improve patient safety and decision-making during surgeries. Existing state-of-the-art methods for surgical step recognition either rely on separate, multi-stage modeling of spatial and temporal information or operate on short-range temporal resolution when learned jointly. However, the benefits of joint modeling of spatio-temporal features and long-range information are not taken in account. In this paper, we propose a vision transformer-based approach to jointly learn spatio-temporal features directly from sequence of frame-level patches. Our method incorporates a gated-temporal attention mechanism that intelligently combines short-term and long-term spatio-temporal feature representations. We extensively evaluate our approach on two cataract surgery video datasets, namely Cataract-101 and D99, and demonstrate superior performance compared to various state-of-the-art methods. These results validate the suitability of our proposed approach for automated surgical step recognition. Our code is released at: https://github.com/nisargshah1999/GLSFormer

Video-based assessment of intraoperative surgical skill

Purpose: The objective of this investigation is to provide a comprehensive analysis of state-of-the-art methods for video-based assessment of surgical skill in the operating room. Methods: Using a data set of 99 videos of capsulorhexis, a critical step in cataract surgery, we evaluate feature based methods previously developed for surgical skill assessment mostly under benchtop settings. In addition, we present and validate two deep learning methods that directly assess skill using RGB videos. In the first method, we predict instrument tips as keypoints, and learn surgical skill using temporal convolutional neural networks. In the second method, we propose a novel architecture for surgical skill assessment that includes a frame-wise encoder (2D convolutional neural network) followed by a temporal model (recurrent neural network), both of which are augmented by visual attention mechanisms. We report the area under the receiver operating characteristic curve, sensitivity, specificity, and predictive values with each method through 5-fold cross-validation. Results: For the task of binary skill classification (expert vs. novice), deep neural network based methods exhibit higher AUC than the classical spatiotemporal interest point based methods. The neural network approach using attention mechanisms also showed high sensitivity and specificity. Conclusion: Deep learning methods are necessary for video-based assessment of surgical skill in the operating room. Our findings of internal validity of a network using attention mechanisms to assess skill directly using RGB videos should be evaluated for external validity in other data sets.

Real-time Teaching Cues for Automated Surgical Coaching

With introduction of new technologies in the operating room like the da Vinci Surgical System, training surgeons to use them effectively and efficiently is crucial in the delivery of better patient care. Coaching by an expert surgeon is effective in teaching relevant technical skills, but current methods to deliver effective coaching are limited and not scalable. We present a virtual reality simulation-based framework for automated virtual coaching in surgical education. We implement our framework within the da Vinci Skills Simulator. We provide three coaching modes ranging from a hands-on teacher (continuous guidance) to a handsoff guide (assistance upon request). We present six teaching cues targeted at critical learning elements of a needle passing task, which are shown to the user based on the coaching mode. These cues are graphical overlays which guide the user, inform them about sub-par performance, and show relevant video demonstrations. We evaluated our framework in a pilot randomized controlled trial with 16 subjects in each arm. In a post-study questionnaire, participants reported high comprehension of feedback, and perceived improvement in performance. After three practice repetitions of the task, the control arm (independent learning) showed better motion efficiency whereas the experimental arm (received real-time coaching) had better performance of learning elements (as per the ACS Resident Skills Curriculum). We observed statistically higher improvement in the experimental group based on one of the metrics (related to needle grasp orientation). In conclusion, we developed an automated coach that provides real-time cues for surgical training and demonstrated its feasibility.

Recognizing Surgical Activities with Recurrent Neural Networks

We apply recurrent neural networks to the task of recognizing surgical activities from robot kinematics. Prior work in this area focuses on recognizing short, low-level activities, or gestures, and has been based on variants of hidden Markov models and conditional random fields. In contrast, we work on recognizing both gestures and longer, higher-level activites, or maneuvers, and we model the mapping from kinematics to gestures/maneuvers with recurrent neural networks. To our knowledge, we are the first to apply recurrent neural networks to this task. Using a single model and a single set of hyperparameters, we match state-of-the-art performance for gesture recognition and advance state-of-the-art performance for maneuver recognition, in terms of both accuracy and edit distance. Code is available at https://github.com/rdipietro/miccai-2016-surgical-activity-rec .

Automated Objective Surgical Skill Assessment in the Operating Room Using Unstructured Tool Motion

Previous work on surgical skill assessment using intraoperative tool motion in the operating room (OR) has focused on highly-structured surgical tasks such as cholecystectomy. Further, these methods only considered generic motion metrics such as time and number of movements, which are of limited instructive value. In this paper, we developed and evaluated an automated approach to the surgical skill assessment of nasal septoplasty in the OR. The obstructed field of view and highly unstructured nature of septoplasty precludes trainees from efficiently learning the procedure. We propose a descriptive structure of septoplasty consisting of two types of activity: (1) brushing activity directed away from the septum plane characterizing the consistency of the surgeon's wrist motion and (2) activity along the septal plane characterizing the surgeon's coverage pattern. We derived features related to these two activity types that classify a surgeon's level of training with an average accuracy of about 72%. The features we developed provide surgeons with personalized, actionable feedback regarding their tool motion.