複数データ源の深層学習による手術サブタスクの時間的セグメンテーション
Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources
手術ロボットの運動学・視覚・システムイベントを統合したFusion-KVEモデルで手術タスクの状態をリアルタイム推定し、最大89.4%の精度を達成した研究。
著者: Yidan Qin, Sahba Aghajani Pedram, Seyedshams Feyzabadi, Max Allan, A. Jonathan McLeod, Joel W. Burdick, Mahdi Azizian
分類: cs.CV, cs.LG, cs.RO, eess.IV
原文アブストラクト
Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an observation (such as bleeding). A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs. The objective of this work is to estimate the current state of the surgical task based on the actions performed or events occurred as the task progresses. We propose Fusion-KVE, a unified surgical state estimation model that incorporates multiple data sources including the Kinematics, Vision, and system Events. Additionally, we examine the strengths and weaknesses of different state estimation models in segmenting states with different representative features or levels of granularity. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), as well as a more complex dataset involving robotic intra-operative ultrasound (RIOUS) imaging, created using the da Vinci Xi surgical system. Our model achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgical state estimation models in both JIGSAWS suturing dataset and our RIOUS dataset.
