Deep learning has been widely considered in medical image segmentation. However, the difficulty of acquiring medical images and labels can affect the accuracy of the segmentation results for deep learning methods. In this paper, an automatic segmentation method is proposed by devising a multicomponent neighborhood extreme learning machine to improve the boundary attention region of the preliminary segmentation results. The neighborhood features are acquired by training U-Nets with the multicomponent small dataset, which consists of original thyroid ultrasound images, Sobel edge images and superpixel images. Afterward, the neighborhood features are selected by min-redundancy and max-relevance filter in the designed extreme learning machine, and the selected features are used to train the extreme learning machine to obtain supplementary segmentation results. Finally, the accuracy of the segmentation results is improved by adjusting the boundary attention region of the preliminary segmentation results with the supplementary segmentation results. This method combines the advantages of deep learning and traditional machine learning, boosting the accuracy of thyroid segmentation accuracy with a small dataset in a multigroup test.

深度学习在医学图像分割中得到了广泛的考虑。然而，获取医学图像和标签的难度会影响深度学习方法分割结果的准确性。本文提出了一种自动分割方法，通过设计多分量邻域极限学习机来改善初步分割结果的边界关注区域。邻域特征是通过使用多分量小数据集训练 U-Net 来获得的，该数据集由原始甲状腺超声图像、Sobel 边缘图像和超像素图像组成。然后，在设计的极限学习机中通过最小冗余和最大相关性滤波器选择邻域特征，并使用选择的特征来训练极限学习机以获得补充分割结果。最后，通过补充分割结果调整初步分割结果的边界关注区域，提高分割结果的准确性。该方法结合了深度学习和传统机器学习的优点，在多组测试中以小数据集提高了甲状腺分割的准确性。