基于深度学习的关节镜多光谱场景分割
Arthroscopic Multi-Spectral Scene Segmentation Using Deep Learning
AI总结：
针对膝关节镜手术场景因缺乏判别性纹理特征、成像条件差和标注不足导致分割困难的问题，本文提出一种基于深度学习的多结构场景分割方法，以提升手术部位的自动上下文感知能力。
摘要：
膝关节镜是一种用于治疗膝关节疾病的微创外科手术。由于微型相机获取的手术部位视觉信息不足，使得该手术过程更加复杂。膝关节腔是一个非常受限的空间，因此手术场景是在近距离下拍摄的。膝关节图谱的上下文信息不显著，往往使其难以辨认，结果常常发生非故意的组织损伤，并且培训新外科医生的学习曲线较长。通过对手术部位进行标注来实现自动上下文感知，可以成为缓解这些缺点的一种替代方案。然而，以往的研究证实，手术部位表现出若干局限性，其中包括缺乏具有判别力的上下文信息（如纹理和特征），这极大地限制了该视觉任务。此外，不良的成像条件和缺乏准确的真实标签也限制了精度。为了缓解膝关节镜的这些局限性，本文提出了一种场景分割方法，能够成功分割多个结构。
英文摘要：
Knee arthroscopy is a minimally invasive surgical (MIS) procedure which is performed to treat knee-joint ailment. Lack of visual information of the surgical site obtained from miniaturized cameras make this surgical procedure more complex. Knee cavity is a very confined space; therefore, surgical scenes are captured at close proximity. Insignificant context of knee atlas often makes them unrecognizable as a consequence unintentional tissue damage often occurred and shows a long learning curve to train new surgeons. Automatic context awareness through labeling of the surgical site can be an alternative to mitigate these drawbacks. However, from the previous studies, it is confirmed that the surgical site exhibits several limitations, among others, lack of discriminative contextual information such as texture and features which drastically limits this vision task. Additionally, poor imaging conditions and lack of accurate ground-truth labels are also limiting the accuracy. To mitigate these limitations of knee arthroscopy, in this work we proposed a scene segmentation method that successfully segments multi structures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
