3D- OOCS：使用感应偏差学习前列腺分割
3D-OOCS: Learning Prostate Segmentation with Inductive Bias
摘要：
尽管卷积神经网络（CNN）在3D医学图像分割任务中取得了巨大成功，但目前使用的方法对于不同扫描仪使用的不同协议以及它们产生的各种图像属性或伪影仍然不够鲁棒。为此，我们引入OOCS增强网络，一种新颖的架构，灵感来自脊椎动物的视觉处理的先天性质。以不同的3D U-Net变体为基础，我们将两个3D残差分量添加到第二个编码器块：开和关中心环绕（OOCS）。他们将视网膜中的神经节通路概括为3D设置。在任何标准CNN网络中使用2D-OOCS都可以用尖锐的边缘检测归纳偏差来补充前馈框架。使用3D-OOCS还有助于3D U-Nets以更高的准确性仔细检查和描绘3D图像中存在的解剖结构。我们将最先进的3D U-Nets与其3D-OOCS扩展进行了比较，并显示了后者在3D磁共振图像（MRI）自动前列腺分割中的卓越准确性和鲁棒性。为了进行公平的比较，我们使用相同的管道训练和测试了所有研究的3D U-Net，包括自动超参数优化和数据增强。
英文摘要：
Despite the great success of convolutional neural networks (CNN) in 3D medical image segmentation tasks, the methods currently in use are still not robust enough to the different protocols utilized by different scanners, and to the variety of image properties or artefacts they produce. To this end, we introduce OOCS-enhanced networks, a novel architecture inspired by the innate nature of visual processing in the vertebrates. With different 3D U-Net variants as the base, we add two 3D residual components to the second encoder blocks: on and off center-surround (OOCS). They generalise the ganglion pathways in the retina to a 3D setting. The use of 2D-OOCS in any standard CNN network complements the feedforward framework with sharp edge-detection inductive biases. The use of 3D-OOCS also helps 3D U-Nets to scrutinise and delineate anatomical structures present in 3D images with increased accuracy.We compared the state-of-the-art 3D U-Nets with their 3D-OOCS extensions and showed the superior accuracy and robustness of the latter in automatic prostate segmentation from 3D Magnetic Resonance Images (MRIs). For a fair comparison, we trained and tested all the investigated 3D U-Nets with the same pipeline, including automatic hyperparameter optimisation and data augmentation.
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。
