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Data distribution gaps often pose significant challenges to the use of deep segmentation models. However, retraining models for each distribution is expensive and time-consuming. In clinical contexts, device-embedded algorithms and networks, typically unretrainable and unaccessable post-manufacture, exacerbate this issue. Generative translation methods offer a solution to mitigate the gap by transferring data across domains. However, existing methods mainly focus on intensity distributions while ignoring the gaps due to structure disparities. In this paper, we formulate a new image-to-image translation task to reduce structural gaps. We propose a simple, yet powerful Structure-Unbiased Adversarial (SUA) network which accounts for both intensity and structural differences between the training and test sets for segmentation. It consists of a spatial transformation block followed by an intensity distribution rendering module. The spatial transformation block is proposed to reduce the structural gaps between the two images. The intensity distribution rendering module then renders the deformed structure to an image with the target intensity distribution. Experimental results show that the proposed SUA method has the capability to transfer both intensity distribution and structural content between multiple pairs of datasets and is superior to prior arts in closing the gaps for improving segmentation.

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PURPOSE: The aim of this study was to develop a model-based deep learning architecture to accurately reconstruct fiber orientation distributions (FODs) from a reduced number of diffusion-weighted images (DWIs), facilitating accurate analysis with reduced acquisition times. METHODS: Our proposed architecture, Spherical Deconvolution Network (SDNet), performed FOD reconstruction by mapping 30 DWIs to fully sampled FODs, which have been fit to 288 DWIs. SDNet included DWI-consistency blocks within the network architecture, and a fixel-classification penalty within the loss function. SDNet was trained on a subset of the Human Connectome Project, and its performance compared with FOD-Net, and multishell multitissue constrained spherical deconvolution. RESULTS: SDNet achieved the strongest results with respect to angular correlation coefficient and sum of squared errors. When the impact of the fixel-classification penalty was increased, we observed an improvement in performance metrics reliant on segmenting the FODs into the correct number of fixels. CONCLUSION: Inclusion of DWI-consistency blocks improved reconstruction performance, and the fixel-classification penalty term offered increased control over the angular separation of fixels in the reconstructed FODs.

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Parallel imaging is an important method to accel-erate the acquisition of magnetic resonance imaging data, which can shorten the breath-hold times and reduce motion artifacts. In this paper, we propose a joint frequency domain and image domain (dual-domain) reconstruction method by introducing the full sampling condition for the undersampled multi-coil MR data. The motivation is that the dual domain method can provide more information for accurate image reconstruction. An efficient iterative algorithm is developed based on the variable splitting technique and alternating direction method of multipliers, which is unrolled into an end-to-end trainable deep neural network. We evaluate the proposed network on complex valued multi-coil knee images for both 6-fold and 8-fold acceleration factors, and compare with both variational and deep learning based reconstruction algorithms. The numerical results demonstrate that our method provides better reconstruction accuracy and perceptual quality by making using of the dual domain information. Clinical relevance: This improves the reconstruction quality for accelerated parallel MRI data both visually and quantitatively.

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Cervical cancer has become one of the important factors threatening women's health. Histopathological diagnosis is the most important criterion for cervical cancer diagnosis and treatment. Accurate classification of lesion degree of cervical epithelium by analyzing whole slide images (WSIs) can effectively improve the therapeutic effect and prognosis. However, classification of cervical lesion degree shows poor reproductivity due to lack of standardisation and is subjective among clinicians. In addition, due to the lack of large-scale finely annotated datasets, current deep learning methods do not perform well on this task. In this paper, we propose a two-stage method based on unsupervised pre-training to solve this multi-classification task. Our method first applied a patch-level network to predict the patch-level score and generate a heatmap that can highlight the lesion area. This network is pre-trained using an unsupervised method and verified on a public dataset. Then without extracting manual features, heatmaps are fed into a convolutional neural network (CNN) model directly for the WSI-level prediction. Our approach achieved an accuracy of 81.19% and a custom metric score of 0.9495 on the public cervical cancer WSI dataset, which is the highest in the public so far.

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The use of total marrow and lymphoid irradiation (TMLI) as part of conditioning regimens for bone marrow transplantation is trending due to its advantages in disease control and low toxicity. Accurate contouring of target structures such as bone and lymph nodes plays an important role in irradiation planning. However, this process is often time-consuming and prone to inter-observer variation. Recently, deep learning methods such as convolutional neural networks (CNNs) and vision transformers have achieved tremendous success in medical image segmentation, therefore enabling fast semiautomatic radiotherapy planning. In this paper, we propose a dual-encoder U-shaped model named DE-Net, to automatically segment the target structures for TMLI. To enhance the learned features, the encoder of DE-Net is composed of parallel CNNs and vision transformers, which can model both local and global contexts. The multi-level features from the two branches are progressively fused by intermediate modules, therefore effectively preserving low-level details. Our experiments demonstrate that the proposed method achieves state-of-the-art results and a significant improvement in lymph node segmentation compared with existing methods.

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