Unsupervised binocular depth prediction network for laparoscopic surgery.

IF 1.5 4区 医学 Q3 SURGERY Computer Assisted Surgery Pub Date : 2019-01-01 DOI:10.1080/24699322.2018.1560082
Ke Xu, Zhiyong Chen, F. Jia
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引用次数: 0

Abstract

Minimally invasive surgery (MIS) is characterized by less trauma, shorter recovery time, and lower postoperative infection rate. The two-dimensional (2D) laparoscopic imaging lacks depth perception and does not provide quantitative depth information, thereby limiting precise and complex surgical operations. Three-dimensional (3D) laparoscopic imaging provides surgeons depth perception. This study aims to 3D reconstruction of the surgical scene based on the disparity map generated by the depth estimation algorithm. An unsupervised learning autoencoder method was proposed to calculate the accurate disparity with a 101-layer residual convolutional network. The loss function included three parts: left-right consistency loss, structure similarity loss, and reconstruction error loss, the combination can improve reconstruction accuracy and robustness. The method was validated on a Hamlyn Center Laparoscopic/Endoscopic Video Dataset. The structural similarity index (SSIM) is 0.8349 ± 0.0523 and the peak signal-to-noise ratio (PSNR) is 14.4957 ± 1.9676. The depth prediction network has high accuracy and robustness. The average time to produce each disparity map is about 16 ms. The experimental result shows that the proposed depth estimation method can offer dense disparity map, and can meet surgical real-time requirement. Future work will focus on network structure optimization and loss function design, transfer learning to improve the robustness and accuracy further.
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腹腔镜手术无监督双目深度预测网络。
微创手术具有创伤小、恢复时间短、术后感染率低等特点。二维(2D)腹腔镜成像缺乏深度感知,不能提供定量的深度信息,从而限制了精确和复杂的外科手术。三维(3D)腹腔镜成像为外科医生提供深度感知。本研究旨在基于深度估计算法生成的视差图对手术场景进行三维重建。提出了一种基于101层残差卷积网络的无监督学习自编码器方法来精确计算视差。损失函数包括左右一致性损失、结构相似度损失和重构误差损失三部分,结合使用可以提高重构精度和鲁棒性。该方法在Hamlyn中心腹腔镜/内窥镜视频数据集上进行了验证。结构相似指数(SSIM)为0.8349±0.0523,峰值信噪比(PSNR)为14.4957±1.9676。该深度预测网络具有较高的精度和鲁棒性。生成每个视差图的平均时间约为16毫秒。实验结果表明,所提出的深度估计方法能够提供密集的视差图,满足手术实时性的要求。未来的工作将集中在网络结构优化、损失函数设计、迁移学习等方面,进一步提高鲁棒性和准确性。
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来源期刊
Computer Assisted Surgery
Computer Assisted Surgery Medicine-Surgery
CiteScore
2.30
自引率
0.00%
发文量
13
审稿时长
10 weeks
期刊介绍: omputer Assisted Surgery aims to improve patient care by advancing the utilization of computers during treatment; to evaluate the benefits and risks associated with the integration of advanced digital technologies into surgical practice; to disseminate clinical and basic research relevant to stereotactic surgery, minimal access surgery, endoscopy, and surgical robotics; to encourage interdisciplinary collaboration between engineers and physicians in developing new concepts and applications; to educate clinicians about the principles and techniques of computer assisted surgery and therapeutics; and to serve the international scientific community as a medium for the transfer of new information relating to theory, research, and practice in biomedical imaging and the surgical specialties. The scope of Computer Assisted Surgery encompasses all fields within surgery, as well as biomedical imaging and instrumentation, and digital technology employed as an adjunct to imaging in diagnosis, therapeutics, and surgery. Topics featured include frameless as well as conventional stereotactic procedures, surgery guided by intraoperative ultrasound or magnetic resonance imaging, image guided focused irradiation, robotic surgery, and any therapeutic interventions performed with the use of digital imaging technology.
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