It is difficult for general registration methods to establish the fine correspondence between images with complex anatomical structures. To overcome the above problem, this work presents SFM-Net, an unsupervised multi-stage semantic feature-based network. In addition to using the pixel-based similarity metrics, we propose a feature operator and emphasize a feature registration to improve the alignment of semantic related areas. Specifically, we design a two-stage training strategy, the intensity image registration stage and the semantic feature registration stage. The former is for valid semantic features learning and intensity-based coarse registration, while the latter is for semantic areas alignment, achieving fine transformation of anatomical structure. The same structure of both stages is composed of a dual-stream feature extraction module (DFEM) and a refined deformation field generation module (RDGM). Unlike the deep learning-based approaches that utilizing down-sampled encoder to extract features, DFEM constructed by dual-stream U-Net structure can capture semantic information in decoder feature for structural alignment. Different with approaches applying cascaded networks to learn deformation field, our proposed RDGM generates multi-scale deformation fields by performing a coarse-to-fine registration within a single network. Experiments on 3D brain MRI and liver CT datasets confirm that the proposed SFM-Net achieves accurate and diffeomorphic registration results, outperforming other state-of-the-art methods.

Deep learning-based image registration (DLIR) meth-ods have achieved remarkable success in deformable im-age registration. We observe that iterative inference can exploit the well-trained registration network to the fullest extent. In this work, we propose a novel Iterative Inference Residual Pyramid Network (IIRP-Net) to enhance registration performance without any additional training costs. In IIRP-Net, we construct a streamlined pyramid registration network consisting of a feature extractor and residual flow estimators (RP-Net) to achieve generalized capabilities in feature extraction and registration. Then, in the inference phase, IIRP-Net employs an iterative inference strategy to enhance RP-Net by iteratively reutilizing residual flow es-timators from coarse to fine. The number of iterations is adaptively determined by the proposed IterStop mecha-nism. We conduct extensive experiments on the FLARE and Mindboggle datasets and the results verify the effectiveness of the proposed method, outperforming state-of-the-art de-formable image registration methods. Our code is available at https://github.com/Torbjorn1997/IIRP-Net.

In deformable image registration, learning-based methods have demonstrated impressive performance. However, previous methods mainly focus on enhancing the capability to predict the deformation field, without fully exploring the optimal ways to update the deformation field. As a result, errors can easily accumulate during the process of updating the deformation field, thus leading to inaccurate registration results. To solve this problem, we propose RC-Block (Refinement Coefficient Block) to find the optimal way to update the deformation field, thus achieving high accuracy registration results. The proposed module introduces a novel refinement coefficient, which can leverage information from the current scale to continuously rectify the previous deformation field. In addition, RC-Block is a plug-and-play module that can be seamlessly and easily integrated into most registration methods. Extensive experimental results show that our module can effectively improve existing advanced methods’ performance with minimal additional burden.

Deformable registration of multi-modal medical images is a challenging task in medical image processing due to the differences in both appearance and structure. We propose an unsupervised hierarchical translation-based model to perform a coarse to fine registration of multi-modal medical images. The proposed model consists of three parts: a coarse registration network, a modal translation network and a fine registration network. First, the coarse registration network learns to obtain the coarse deformation field, which is applied as structure-preserving information to generate a translated image by the modal translation network. Then, the translated image as enhancing information combined with the original images are used to derive a fine deformation field in the fine registration network. Furthermore, the final deformation field is composed from the coarse and the fine deformation fields. In this way, the proposed model can learn high accurate deformation field to implement multi-modal medical image registration. Experiments on two multi-modal brain image datasets demonstrate the effectiveness of this model.

Recently, several Transformer-based methods have been presented to improve image segmentation. However, since Transformer needs regular square images and has difficulty in obtaining local feature information, the performance of image segmentation is seriously affected. In this paper, we propose a novel encoder-decoder network named TCRNet, which makes Transformer, Convolutional neural network (CNN) and Recurrent neural network (RNN) complement each other. In the encoder, we extract and concatenate the feature maps from Transformer and CNN to effectively capture global and local feature information of images. Then in the decoder, we utilize convolutional RNN in the proposed recurrent decoding unit to refine the feature maps from the decoder for finer prediction. Experimental results on three medical datasets demonstrate that TCRNet effectively improves the segmentation precision.