  Deep convolutional neural networks have achieved remarkable progress on a
variety of medical image computing tasks. A common problem when applying
supervised deep learning methods to medical images is the lack of labeled data,
which is very expensive and time-consuming to be collected. In this paper, we
present a novel semi-supervised method for medical image segmentation, where
the network is optimized by the weighted combination of a common supervised
loss for labeled inputs only and a regularization loss for both labeled and
unlabeled data. To utilize the unlabeled data, our method encourages the
consistent predictions of the network-in-training for the same input under
different regularizations. Aiming for the semi-supervised segmentation problem,
we enhance the effect of regularization for pixel-level predictions by
introducing a transformation, including rotation and flipping, consistent
scheme in our self-ensembling model. With the aim of semi-supervised
segmentation tasks, we introduce a transformation consistent strategy in our
self-ensembling model to enhance the regularization effect for pixel-level
predictions. We have extensively validated the proposed semi-supervised method
on three typical yet challenging medical image segmentation tasks: (i) skin
lesion segmentation from dermoscopy images on International Skin Imaging
Collaboration (ISIC) 2017 dataset, (ii) optic disc segmentation from fundus
images on Retinal Fundus Glaucoma Challenge (REFUGE) dataset, and (iii) liver
segmentation from volumetric CT scans on Liver Tumor Segmentation Challenge
(LiTS) dataset. Compared to the state-of-the-arts, our proposed method shows
superior segmentation performance on challenging 2D/3D medical images,
demonstrating the effectiveness of our semi-supervised method for medical image
segmentation.
