Deep learning-based depth estimation from a synthetic endoscopy image training set
Deep learning-based depth estimation from a synthetic endoscopy image training set
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DOI：
10.1117/12.2293785
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发表时间：
2018-03
影响因子：
1.3
通讯作者：
Faisal Mahmood;N. Durr
中科院分区：
文献类型：
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作者：
Faisal Mahmood;N. Durr
结直肠癌是全球第四大癌症死亡原因。通过内镜结肠镜检查发现并切除癌前病变是降低结直肠癌死亡率的最有效方法。不幸的是，传统的结肠镜检查有几乎25%的息肉漏诊率，部分原因是缺乏深度信息和结肠表面的对比度。由于内窥镜尺寸有限和粘膜可变形，使用常规硬件和软件方法估计深度在内窥镜检查中具有挑战性。在这项工作中，我们使用联合深度学习和基于图形模型的框架来进行内窥镜图像的深度估计。由于深度是对象的固有连续属性，因此可以很容易地将其视为连续图形学习问题。与以前的方法不同，这种方法不需要手工制作的功能。需要大量的增强数据来训练这样的框架。由于具有真实深度图的结肠镜图像的可用性有限，并且结肠纹理具有高度的患者特异性，因此我们使用合成的无纹理结肠体模来生成训练图像以训练我们的模型。初步结果表明，我们的系统可以估计深度的幻影测试数据的相对误差为0.164。由此产生的深度图可能被证明对3D重建和自动计算机辅助检测（CAD）有价值，以帮助识别病变。
Colorectal cancer is the fourth leading cause of cancer deaths worldwide. The detection and removal of premalignant lesions through an endoscopic colonoscopy is the most effective way to reduce colorectal cancer mortality. Unfortunately, conventional colonoscopy has an almost 25% polyp miss rate, in part due to the lack of depth information and contrast of the surface of the colon. Estimating depth using conventional hardware and software methods is challenging in endoscopy due to limited endoscope size and deformable mucosa. In this work, we use a joint deep learning and graphical model-based framework for depth estimation from endoscopy images. Since depth is an inherently continuous property of an object, it can easily be posed as a continuous graphical learning problem. Unlike previous approaches, this method does not require hand-crafted features. Large amounts of augmented data are required to train such a framework. Since there is limited availability of colonoscopy images with ground-truth depth maps and colon texture is highly patient-specific, we generated training images using a synthetic, texture-free colon phantom to train our models. Initial results show that our system can estimate depths for phantom test data with a relative error of 0.164. The resulting depth maps could prove valuable for 3D reconstruction and automated Computer Aided Detection (CAD) to assist in identifying lesions.