Shahedi Maysam, Halicek Martin, Dormer James D, Fei Baowei
Department of Bioengineering, The Univ. of Texas at Dallas, TX.
Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA.
Proc SPIE Int Soc Opt Eng. 2020 Feb;11313. doi: 10.1117/12.2549716. Epub 2020 Mar 10.
Computer-assisted image segmentation techniques could help clinicians to perform the border delineation task faster with lower inter-observer variability. Recently, convolutional neural networks (CNNs) are widely used for automatic image segmentation. In this study, we used a technique to involve observer inputs for supervising CNNs to improve the accuracy of the segmentation performance. We added a set of sparse surface points as an additional input to supervise the CNNs for more accurate image segmentation. We tested our technique by applying minimal interactions to supervise the networks for segmentation of the prostate on magnetic resonance images. We used U-Net and a new network architecture that was based on U-Net (dual-input path [DIP] U-Net), and showed that our supervising technique could significantly increase the segmentation accuracy of both networks as compared to fully automatic segmentation using U-Net. We also showed DIP U-Net outperformed U-Net for supervised image segmentation. We compared our results to the measured inter-expert observer difference in manual segmentation. This comparison suggests that applying about 15 to 20 selected surface points can achieve a performance comparable to manual segmentation.
计算机辅助图像分割技术可以帮助临床医生更快地完成边界划定任务，且观察者间的变异性更低。最近，卷积神经网络（CNN）被广泛用于自动图像分割。在本研究中，我们使用了一种纳入观察者输入以监督CNN的技术，来提高分割性能的准确性。我们添加了一组稀疏表面点作为额外输入，以监督CNN进行更准确的图像分割。我们通过应用最少的交互来监督网络对磁共振图像上的前列腺进行分割，从而测试了我们的技术。我们使用了U-Net和一种基于U-Net的新网络架构（双输入路径[DIP]U-Net），结果表明，与使用U-Net进行的全自动分割相比，我们的监督技术能够显著提高两个网络的分割准确性。我们还表明，在监督图像分割方面，DIP U-Net优于U-Net。我们将我们的结果与手动分割中测量的专家间观察者差异进行了比较。这种比较表明，应用大约15到20个选定的表面点可以实现与手动分割相当的性能。
