Manish Kumar Sahu

Jecia Z. Y. Mao, Francis X Creighton, Russell H Taylor et al.

Accurate segmentation and tracking of relevant elements of the surgical scene is crucial to enable context-aware intraoperative assistance and decision making. Current solutions remain tethered to domain-specific, supervised models that rely on labeled data and required domain-specific data to adapt to new surgical scenarios and beyond predefined label categories. Recent advances in prompt-driven vision foundation models (VFM) have enabled open-set, zero-shot segmentation across heterogeneous medical images. However, dependence of these models on manual visual or textual cues restricts their deployment in introperative surgical settings. We introduce a speech-guided collaborative perception (SCOPE) framework that integrates reasoning capabilities of large language model (LLM) with perception capabilities of open-set VFMs to support on-the-fly segmentation, labeling and tracking of surgical instruments and anatomy in intraoperative video streams. A key component of this framework is a collaborative perception agent, which generates top candidates of VFM-generated segmentation and incorporates intuitive speech feedback from clinicians to guide the segmentation of surgical instruments in a natural human-machine collaboration paradigm. Afterwards, instruments themselves serve as interactive pointers to label additional elements of the surgical scene. We evaluated our proposed framework on a subset of publicly available Cataract1k dataset and an in-house ex-vivo skull-base dataset to demonstrate its potential to generate on-the-fly segmentation and tracking of surgical scene. Furthermore, we demonstrate its dynamic capabilities through a live mock ex-vivo experiment. This human-AI collaboration paradigm showcase the potential of developing adaptable, hands-free, surgeon-centric tools for dynamic operating-room environments.

Manish Sahu, Anirban Mukhopadhyay, Stefan Zachow

Purpose: Segmentation of surgical instruments in endoscopic videos is essential for automated surgical scene understanding and process modeling. However, relying on fully supervised deep learning for this task is challenging because manual annotation occupies valuable time of the clinical experts. Methods: We introduce a teacher-student learning approach that learns jointly from annotated simulation data and unlabeled real data to tackle the erroneous learning problem of the current consistency-based unsupervised domain adaptation framework. Results: Empirical results on three datasets highlight the effectiveness of the proposed framework over current approaches for the endoscopic instrument segmentation task. Additionally, we provide analysis of major factors affecting the performance on all datasets to highlight the strengths and failure modes of our approach. Conclusion: We show that our proposed approach can successfully exploit the unlabeled real endoscopic video frames and improve generalization performance over pure simulation-based training and the previous state-of-the-art. This takes us one step closer to effective segmentation of surgical tools in the annotation scarce setting.

Manish Sahu, Ronja Strömsdörfer, Anirban Mukhopadhyay et al.

Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems. Recent success of image-based solutions using fully-supervised deep learning approaches can be attributed to the collection of big labeled datasets. However, the annotation of a big dataset of real videos can be prohibitively expensive and time consuming. Computer simulations could alleviate the manual labeling problem, however, models trained on simulated data do not generalize to real data. This work proposes a consistency-based framework for joint learning of simulated and real (unlabeled) endoscopic data to bridge this performance generalization issue. Empirical results on two data sets (15 videos of the Cholec80 and EndoVis'15 dataset) highlight the effectiveness of the proposed \emph{Endo-Sim2Real} method for instrument segmentation. We compare the segmentation of the proposed approach with state-of-the-art solutions and show that our method improves segmentation both in terms of quality and quantity.