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Register a new userA low-rank representation for unsupervised registration of medical images
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Added by
Dengqiang Jia
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Registration networks have shown great application potentials in medical image analysis. However, supervised training methods have a great demand for large and high-quality labeled datasets, which is time-consuming and sometimes impractical due to data sharing issues. Unsupervised image registration algorithms commonly employ intensity-based similarity measures as loss functions without any manual annotations. These methods estimate the parameterized transformations between pairs of moving and fixed images through the optimization of the network parameters during training. However, these methods become less effective when the image quality varies, e.g., some images are corrupted by substantial noise or artifacts. In this work, we propose a novel approach based on a low-rank representation, i.e., Regnet-LRR, to tackle the problem. We project noisy images into a noise-free low-rank space, and then compute the similarity between the images. Based on the low-rank similarity measure, we train the registration network to predict the dense deformation fields of noisy image pairs. We highlight that the low-rank projection is reformulated in a way that the registration network can successfully update gradients. With two tasks, i.e., cardiac and abdominal intra-modality registration, we demonstrate that the low-rank representation can boost the generalization ability and robustness of models as well as bring significant improvements in noisy data registration scenarios.
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Local discriminative representation is needed in many medical image analysis tasks such as identifying sub-types of lesion or segmenting detailed components of anatomical structures. However, the commonly applied supervised representation learning methods require a large amount of annotated data, and unsupervised discriminative representation learning distinguishes different images by learning a global feature, both of which are not suitable for localized medical image analysis tasks. In order to avoid the limitations of these two methods, we introduce local discrimination into unsupervised representation learning in this work. The model contains two branches: one is an embedding branch which learns an embedding function to disperse dissimilar pixels over a low-dimensional hypersphere; and the other is a clustering branch which learns a clustering function to classify similar pixels into the same cluster. These two branches are trained simultaneously in a mutually beneficial pattern, and the learnt local discriminative representations are able to well measure the similarity of local image regions. These representations can be transferred to enhance various downstream tasks. Meanwhile, they can also be applied to cluster anatomical structures from unlabeled medical images under the guidance of topological priors from simulation or other structures with similar topological characteristics. The effectiveness and usefulness of the proposed method are demonstrated by enhancing various downstream tasks and clustering anatomical structures in retinal images and chest X-ray images.
Deformable image registration is a fundamental task in medical imaging. Due to the large computational complexity of deformable registration of volumetric images, conventional iterative methods usually face the tradeoff between the registration accuracy and the computation time in practice. In order to boost the registration performance in both accuracy and runtime, we propose a fast convolutional neural network. Specially, to efficiently utilize the memory resources and enlarge the model capacity, we adopt additive forwarding instead of channel concatenation and deepen the network in each encoder and decoder stage. To facilitate the learning efficiency, we leverage skip connection within the encoder and decoder stages to enable residual learning and employ an auxiliary loss at the bottom layer with lowest resolution to involve deep supervision. Particularly, the low-resolution auxiliary loss is weighted by an exponentially decayed parameter during the training phase. In conjunction with the main loss in high-resolution grid, a coarse-to-fine learning strategy is achieved. Last but not least, we introduce an auxiliary loss based on the segmentation prior to improve the registration performance in Dice score. Comparing to the auxiliary loss using average Dice score, the proposed multi-label segmentation loss does not induce additional memory cost in the training phase and can be employed on images with arbitrary amount of categories. In the experiments, we show FDRN outperforms the existing state-of-the-art registration methods for brain MR images by resorting to the compact network structure and efficient learning. Besides, FDRN is a generalized framework for image registration which is not confined to a particular type of medical images or anatomy.
Contrastive representation learning is an effective unsupervised method to alleviate the demand for expensive annotated data in medical image processing. Recent work mainly based on instance-wise discrimination to learn global features, while neglect local details, which limit their application in processing tiny anatomical structures, tissues and lesions. Therefore, we aim to propose a universal local discrmination framework to learn local discriminative features to effectively initialize medical models, meanwhile, we systematacially investigate its practical medical applications. Specifically, based on the common property of intra-modality structure similarity, i.e. similar structures are shared among the same modality images, a systematic local feature learning framework is proposed. Instead of making instance-wise comparisons based on global embedding, our method makes pixel-wise embedding and focuses on measuring similarity among patches and regions. The finer contrastive rule makes the learnt representation more generalized for segmentation tasks and outperform extensive state-of-the-art methods by wining 11 out of all 12 downstream tasks in color fundus and chest X-ray. Furthermore, based on the property of inter-modality shape similarity, i.e. structures may share similar shape although in different medical modalities, we joint across-modality shape prior into region discrimination to realize unsupervised segmentation. It shows the feaibility of segmenting target only based on shape description from other modalities and inner pattern similarity provided by region discrimination. Finally, we enhance the center-sensitive ability of patch discrimination by introducing center-sensitive averaging to realize one-shot landmark localization, this is an effective application for patch discrimination.
In this paper, we propose a novel classification scheme for the remotely sensed hyperspectral image (HSI), namely SP-DLRR, by comprehensively exploring its unique characteristics, including the local spatial information and low-rankness. SP-DLRR is mainly composed of two modules, i.e., the classification-guided superpixel segmentation and the discriminative low-rank representation, which are iteratively conducted. Specifically, by utilizing the local spatial information and incorporating the predictions from a typical classifier, the first module segments pixels of an input HSI (or its restoration generated by the second module) into superpixels. According to the resulting superpixels, the pixels of the input HSI are then grouped into clusters and fed into our novel discriminative low-rank representation model with an effective numerical solution. Such a model is capable of increasing the intra-class similarity by suppressing the spectral variations locally while promoting the inter-class discriminability globally, leading to a restored HSI with more discriminative pixels. Experimental results on three benchmark datasets demonstrate the significant superiority of SP-DLRR over state-of-the-art methods, especially for the case with an extremely limited number of training pixels.
Deformable image registration is widely utilized in medical image analysis, but most proposed methods fail in the situation of complex deformations. In this paper, we pre-sent a cascaded feature warping network to perform the coarse-to-fine registration. To achieve this, a shared-weights encoder network is adopted to generate the feature pyramids for the unaligned images. The feature warping registration module is then used to estimate the deformation field at each level. The coarse-to-fine manner is implemented by cascading the module from the bottom level to the top level. Furthermore, the multi-scale loss is also introduced to boost the registration performance. We employ two public benchmark datasets and conduct various experiments to evaluate our method. The results show that our method outperforms the state-of-the-art methods, which also demonstrates that the cascaded feature warping network can perform the coarse-to-fine registration effectively and efficiently.
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