Department of Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden.
Department of Neurological Surgery, University of California San Fransisco, San Francisco, CA 94143-0112, USA.
Sensors (Basel). 2022 Jul 15;22(14):5292. doi: 10.3390/s22145292.
In most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annotation puts high demands on medical personnel. The aim of this study is to train a deep network for segmentation by using ellipse box areas surrounding the tumors. In the proposed method, the deep network is trained by using a large number of unannotated tumor images with foreground (FG) and background (BG) ellipse box areas surrounding the tumor and background, and a small number of patients (<20) with annotated tumors. The training is conducted by initial training on two ellipse boxes on unannotated MRIs, followed by refined training on a small number of annotated MRIs. We use a multi-stream U-Net for conducting our experiments, which is an extension of the conventional U-Net. This enables the use of complementary information from multi-modality (e.g., T1, T1ce, T2, and FLAIR) MRIs. To test the feasibility of the proposed approach, experiments and evaluation were conducted on two datasets for glioma segmentation. Segmentation performance on the test sets is then compared with those used on the same network but trained entirely by annotated MRIs. Our experiments show that the proposed method has obtained good tumor segmentation results on the test sets, wherein the dice score on tumor areas is (0.8407, 0.9104), and segmentation accuracy on tumor areas is (83.88%, 88.47%) for the MICCAI BraTS’17 and US datasets, respectively. Comparing the segmented results by using the network trained by all annotated tumors, the drop in the segmentation performance from the proposed approach is (0.0594, 0.0159) in the dice score, and (8.78%, 2.61%) in segmented tumor accuracy for MICCAI and US test sets, which is relatively small. Our case studies have demonstrated that training the network for segmentation by using ellipse box areas in place of all annotated tumors is feasible, and can be considered as an alternative, which is a trade-off between saving medical experts’ time annotating tumors and a small drop in segmentation performance.
在大多数基于深度学习的脑肿瘤分割方法中，训练深度网络需要标注肿瘤区域。然而，准确的肿瘤标注对医务人员提出了很高的要求。本研究的目的是使用围绕肿瘤和背景的椭圆形框区域来训练分割的深度网络。在提出的方法中，通过使用大量带有肿瘤前景（FG）和背景（BG）椭圆形框区域的未标注肿瘤图像以及少量带有标注肿瘤的患者（<20）来训练深度网络。训练是通过在两个未标注的 MRI 上的初始训练椭圆框开始的，然后在少量标注的 MRI 上进行精细训练。我们使用多流 U-Net 进行实验，这是常规 U-Net 的扩展。这使得能够利用多模态（例如，T1、T1ce、T2 和 FLAIR）MRI 的互补信息。为了测试所提出方法的可行性，我们在两个用于脑肿瘤分割的数据集上进行了实验和评估。然后将测试集上的分割性能与在相同网络上但完全由标注 MRI 训练的分割性能进行比较。我们的实验表明，所提出的方法在测试集上获得了良好的肿瘤分割结果，其中肿瘤区域的骰子分数为（0.8407，0.9104），肿瘤区域的分割准确率为（83.88%，88.47%），分别为 MICCAI BraTS'17 和 US 数据集。与使用所有标注肿瘤训练的网络的分割结果进行比较，所提出方法的分割性能下降（0.0594，0.0159）在骰子分数中，以及（8.78%，2.61%）在 MICCAI 和 US 测试集中的分割肿瘤准确率，相对较小。我们的案例研究表明，使用椭圆形框区域训练分割网络而不是所有标注的肿瘤是可行的，可以被视为一种替代方案，这是在节省医学专家标注肿瘤的时间和分割性能略有下降之间的权衡。
