Semi-weakly-supervised neural network training for medical image
  registration
Yiwen Li Yunguan Fu Iani J. M. B. Gayo Qianye Yang Zhe Min Shaheer U. Saeed Wen Yan Yipei Wang J. Alison Noble Mark Emberton Matthew J. Clarkson Dean C. Barratt Victor A. Prisacariu Yipeng Hu
For training registration networks, weak supervision from segmented
corresponding regions-of-interest (ROIs) have been proven effective for (a)
supplementing unsupervised methods, and (b) being used independently in
registration tasks in which unsupervised losses are unavailable or ineffective.
This correspondence-informing supervision entails cost in annotation that
requires significant specialised effort. This paper describes a
semi-weakly-supervised registration pipeline that improves the model
performance, when only a small corresponding-ROI-labelled dataset is available,
by exploiting unlabelled image pairs. We examine two types of augmentation
methods by perturbation on network weights and image resampling, such that
consistency-based unsupervised losses can be applied on unlabelled data. The
novel WarpDDF and RegCut approaches are proposed to allow commutative
perturbation between an image pair and the predicted spatial transformation
(i.e. respective input and output of registration networks), distinct from
existing perturbation methods for classification or segmentation. Experiments
using 589 male pelvic MR images, labelled with eight anatomical ROIs, show the
improvement in registration performance and the ablated contributions from the
individual strategies. Furthermore, this study attempts to construct one of the
first computational atlases for pelvic structures, enabled by registering
inter-subject MRs, and quantifies the significant differences due to the
proposed semi-weak supervision with a discussion on the potential clinical use
of example atlas-derived statistics.