Deep learning-based fetoscopic mosaicking for field-of-view expansion.
Deep learning-based fetoscopic mosaicking for field-of-view expansion.
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DOI：
10.1007/s11548-020-02242-8
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发表时间：
2020-11
影响因子：
3
通讯作者：
Stoyanov D
中科院分区：
文献类型：
--
作者：
Bano S;Vasconcelos F;Tella-Amo M;Dwyer G;Gruijthuijsen C;Vander Poorten E;Vercauteren T;Ourselin S;Deprest J;Stoyanov D
胎儿镜激光光凝术是一种用于治疗双胎输血综合征 (TTTS) 的微创外科手术，涉及定位和消融胎盘上的异常血管连接，以调节两个胎儿的血流。由于视野有限、能见度差、偶尔出血和图像质量差，该过程特别具有挑战性。胎儿镜镶嵌有助于创建具有扩展视野的图像，这可以在 TTTS 手术期间为临床医生提供便利。我们提出了一种基于深度学习的镶嵌框架，用于从不同设置（例如模拟、幻影、离体和体内环境）捕获的各种胎儿视频。所提出的镶嵌框架通过引入受控数据生成和一致单应性估计模块来扩展现有的深度图像单应性模型来处理视频数据。训练是在一小部分胎儿镜图像上进行的，这些图像独立于测试视频。我们对捕获不同环境的 5 个不同的胎儿视频（2400 帧）进行定量和定性评估。为了证明所提出的框架的鲁棒性，与现有的基于特征的和深度图像单应性方法进行了比较。所提出的马赛克框架优于现有方法并生成有意义的马赛克，同时减少累积漂移，即使存在镜面高光、反射、纹理缺乏和低视频分辨率等视觉挑战。本文的在线版本 (10.1007/s11548-020-02242-8) 包含补充材料，可供授权用户使用。
Fetoscopic laser photocoagulation is a minimally invasive surgical procedure used to treat twin-to-twin transfusion syndrome (TTTS), which involves localization and ablation of abnormal vascular connections on the placenta to regulate the blood flow in both fetuses. This procedure is particularly challenging due to the limited field of view, poor visibility, occasional bleeding, and poor image quality. Fetoscopic mosaicking can help in creating an image with the expanded field of view which could facilitate the clinicians during the TTTS procedure. We propose a deep learning-based mosaicking framework for diverse fetoscopic videos captured from different settings such as simulation, phantoms, ex vivo, and in vivo environments. The proposed mosaicking framework extends an existing deep image homography model to handle video data by introducing the controlled data generation and consistent homography estimation modules. Training is performed on a small subset of fetoscopic images which are independent of the testing videos. We perform both quantitative and qualitative evaluations on 5 diverse fetoscopic videos (2400 frames) that captured different environments. To demonstrate the robustness of the proposed framework, a comparison is performed with the existing feature-based and deep image homography methods. The proposed mosaicking framework outperformed existing methods and generated meaningful mosaic, while reducing the accumulated drift, even in the presence of visual challenges such as specular highlights, reflection, texture paucity, and low video resolution. The online version of this article (10.1007/s11548-020-02242-8) contains supplementary material, which is available to authorized users.
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DOI：
10.1007/s11548-018-1822-7
发表时间：
2018-12
影响因子：
3
作者：
Javaux A;Bouget D;Gruijthuijsen C;Stoyanov D;Vercauteren T;Ourselin S;Deprest J;Denis K;Vander Poorten E
通讯作者：
Vander Poorten E
DOI：
10.1007/s11548-018-1728-4
发表时间：
2018-05
影响因子：
3
作者：
Peter L;Tella-Amo M;Shakir DI;Attilakos G;Wimalasundera R;Deprest J;Ourselin S;Vercauteren T
通讯作者：
Vercauteren T
影响因子：
19.5
作者：
Chadebecq F;Vasconcelos F;Lacher R;Maneas E;Desjardins A;Ourselin S;Vercauteren T;Stoyanov D
通讯作者：
Stoyanov D
DOI：
10.1007/11744023_32
发表时间：
2006-01-01
期刊：
COMPUTER VISION - ECCV 2006 , PT 1, PROCEEDINGS
影响因子：
--
作者：
Bay, Herbert;Tuytelaars, Tinne;Van Gool, Luc
通讯作者：
Van Gool, Luc
影响因子：
5.2
作者：
Dwyer, George;Chadebecq, Francois;Stoyanov, Danail
通讯作者：
Stoyanov, Danail