[论文解读] Focal FCN: Towards Small Object Segmentation with Limited Training Data.

Xiao-Yun Zhou, Mali Shen+3 more|arXiv (Cornell University)|Nov 4, 2017

Aortic aneurysm repair treatments被引用 12

一句话总结

该论文提出了一种用于机器人辅助腔内主动脉修复术中支架移植物的自动3D形状实例化方法，采用等权重焦点U-Net在2D fluoroscopy图像中分割五种定制标记形状，实现了0.6943的平均mIoU和81.01%的标记中心位置误差小于1.6mm，实现了高精度的实时3D导航与机器人路径规划，同时减少了超参数调优的需求。

ABSTRACT

Robot-assisted Fenestrated Endovascular Aortic Repair (FEVAR) is currently navigated by 2D fluoroscopy which is insufficiently informative. Previously, a semi-automatic 3D shape instantiation method was developed to instantiate the 3D shape of a main, deployed, and fenestrated stent graft from a single fluoroscopy projection in real-time, which could help 3D FEVAR navigation and robotic path planning. This proposed semi-automatic method was based on the Robust Perspective-5-Point (RP5P) method, graft gap interpolation and semi-automatic multiple-class marker center determination. In this paper, an automatic 3D shape instantiation could be achieved by automatic multiple-class marker segmentation and hence automatic multiple-class marker center determination. Firstly, the markers were designed into five different shapes. Then, Equally-weighted Focal U-Net was proposed to segment the fluoroscopy projections of customized markers into five classes and hence to determine the marker centers. The proposed Equally-weighted Focal U-Net utilized U-Net as the network architecture, equally-weighted loss function for initial marker segmentation, and then equally-weighted focal loss function for improving the initial marker segmentation. This proposed network outperformed traditional Weighted U-Net on the class-imbalance segmentation in this paper with reducing one hyper-parameter - the weight. An overall mean Intersection over Union (mIoU) of 0.6943 was achieved on 78 testing images, where 81.01% markers were segmented with a center position error <1.6mm. Comparable accuracy of 3D shape instantiation was also achieved and stated. The data, trained models and TensorFlow codes are available on-line.

研究动机与目标

• 为了实现在机器人辅助FEVAR中通过2D fluoroscopy实现支架移植物的自动3D形状实例化，克服人工标记检测的局限性。
• 通过提升分割性能，在训练数据有限的情况下解决标记分割中的类别不平衡问题。
• 通过引入等权重焦点损失策略，消除损失函数中手动权重调优的需求。
• 实现高精度的标记中心定位，以确保准确的3D重建和实时导航。

提出的方法

• 设计了五种不同的标记形状，以提高在fluoroscopy图像中可区分性和分割精度。
• 开发了等权重焦点U-Net，结合U-Net架构与焦点损失函数，以应对类别不平衡问题。
• 该方法采用初始的等权重交叉熵损失，随后使用等权重焦点损失，以优化罕见或小型标记类别的分割。
• 从分割后的标记类别中自动执行标记中心检测，从而实现3D形状实例化流程的完全自动化。
• 在包含定制标记的78张测试fluoroscopy图像上对网络进行了训练与评估。
• 训练好的模型、数据及TensorFlow代码已公开，以支持可复现性与进一步研究。

实验结果

研究问题

• RQ1 在介入治疗的低数据环境下，自动标记分割方法是否能提升3D形状实例化的准确性？
• RQ2 与传统的加权损失函数相比，等权重焦点损失函数在处理小标记分割的类别不平衡问题时表现如何？
• RQ3 所提出的方法在保持高分割性能的同时，能在多大程度上减少超参数调优？
• RQ4 自动标记中心检测能否实现亚毫米级精度，以满足临床级3D重建的需求？

主要发现

• 所提出的等权重焦点U-Net在78张测试图像上实现了0.6943的平均交并比（mIoU），表现出优异的分割性能。
• 81.01%的分割标记中心位置误差小于1.6mm，表明定位精度极高。
• 该方法在类别不平衡分割任务中优于传统的加权U-Net，显著减少了对手动权重调优的需求。
• 该网络在3D形状实例化精度方面与先前半自动方法相当，验证了其临床潜力。
• 通过消除一个超参数（类别权重），简化了模型训练过程，提升了可复现性。
• 数据、模型和代码的公开发布，推动了该方法在医学图像分割领域的广泛应用与进一步拓展。