المؤلفون: Gui, Lujun, Ye, Chuyang, Yan, Tianyi

مصطلحات موضوعية: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition

الوصف: Contrast-enhanced magnetic resonance imaging (MRI) is pivotal in the pipeline of brain tumor segmentation and analysis. Gadolinium-based contrast agents, as the most commonly used contrast agents, are expensive and may have potential side effects, and it is desired to obtain contrast-enhanced brain tumor MRI scans without the actual use of contrast agents. Deep learning methods have been applied to synthesize virtual contrast-enhanced MRI scans from non-contrast images. However, as this synthesis problem is inherently ill-posed, these methods fall short in producing high-quality results. In this work, we propose Conditional Autoregressive Vision Model (CAVM) for improving the synthesis of contrast-enhanced brain tumor MRI. As the enhancement of image intensity grows with a higher dose of contrast agents, we assume that it is less challenging to synthesize a virtual image with a lower dose, where the difference between the contrast-enhanced and non-contrast images is smaller. Thus, CAVM gradually increases the contrast agent dosage and produces higher-dose images based on previous lower-dose ones until the final desired dose is achieved. Inspired by the resemblance between the gradual dose increase and the Chain-of-Thought approach in natural language processing, CAVM uses an autoregressive strategy with a decomposition tokenizer and a decoder. Specifically, the tokenizer is applied to obtain a more compact image representation for computational efficiency, and it decomposes the image into dose-variant and dose-invariant tokens. Then, a masked self-attention mechanism is developed for autoregression that gradually increases the dose of the virtual image based on the dose-variant tokens. Finally, the updated dose-variant tokens corresponding to the desired dose are decoded together with dose-invariant tokens to produce the final contrast-enhanced MRI.
Comment: The work has been accepted by MICCAI 2024

الوصول الحر: http://arxiv.org/abs/2406.16074Test

المؤلفون: Jiang, Qinting, Ye, Chuyang, Wei, Dongyan, Xue, Yuan, Jiang, Jingyan, Wang, Zhi

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia

الوصف: Despite progress, deep neural networks still suffer performance declines under distribution shifts between training and test domains, leading to a substantial decrease in Quality of Experience (QoE) for multimedia applications. Existing test-time adaptation (TTA) methods are challenged by dynamic, multiple test distributions within batches. This work provides a new perspective on analyzing batch normalization techniques through class-related and class-irrelevant features, our observations reveal combining source and test batch normalization statistics robustly characterizes target distributions. However, test statistics must have high similarity. We thus propose Discover Your Neighbours (DYN), the first backward-free approach specialized for dynamic TTA. The core innovation is identifying similar samples via instance normalization statistics and clustering into groups which provides consistent class-irrelevant representations. Specifically, Our DYN consists of layer-wise instance statistics clustering (LISC) and cluster-aware batch normalization (CABN). In LISC, we perform layer-wise clustering of approximate feature samples at each BN layer by calculating the cosine similarity of instance normalization statistics across the batch. CABN then aggregates SBN and TCN statistics to collaboratively characterize the target distribution, enabling more robust representations. Experimental results validate DYN's robustness and effectiveness, demonstrating maintained performance under dynamic data stream patterns.
Comment: 10 pages

الوصول الحر: http://arxiv.org/abs/2406.05413Test

المؤلفون: Zhang, Xinru, Ou, Ni, Basaran, Berke Doga, Visentin, Marco, Qiao, Mengyun, Gu, Renyang, Ouyang, Cheng, Liu, Yaou, Matthew, Paul M., Ye, Chuyang, Bai, Wenjia

مصطلحات موضوعية: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition

الوصف: Brain lesion segmentation plays an essential role in neurological research and diagnosis. As brain lesions can be caused by various pathological alterations, different types of brain lesions tend to manifest with different characteristics on different imaging modalities. Due to this complexity, brain lesion segmentation methods are often developed in a task-specific manner. A specific segmentation model is developed for a particular lesion type and imaging modality. However, the use of task-specific models requires predetermination of the lesion type and imaging modality, which complicates their deployment in real-world scenarios. In this work, we propose a universal foundation model for 3D brain lesion segmentation, which can automatically segment different types of brain lesions for input data of various imaging modalities. We formulate a novel Mixture of Modality Experts (MoME) framework with multiple expert networks attending to different imaging modalities. A hierarchical gating network combines the expert predictions and fosters expertise collaboration. Furthermore, we introduce a curriculum learning strategy during training to avoid the degeneration of each expert network and preserve their specialization. We evaluated the proposed method on nine brain lesion datasets, encompassing five imaging modalities and eight lesion types. The results show that our model outperforms state-of-the-art universal models and provides promising generalization to unseen datasets.
Comment: The work has been early accepted by MICCAI 2024

الوصول الحر: http://arxiv.org/abs/2405.10246Test

المؤلفون: Huijben, Evi M. C., Terpstra, Maarten L., Galapon, Arthur Jr., Pai, Suraj, Thummerer, Adrian, Koopmans, Peter, Afonso, Manya, van Eijnatten, Maureen, Gurney-Champion, Oliver, Chen, Zeli, Zhang, Yiwen, Zheng, Kaiyi, Li, Chuanpu, Pang, Haowen, Ye, Chuyang, Wang, Runqi, Song, Tao, Fan, Fuxin, Qiu, Jingna, Huang, Yixing, Ha, Juhyung, Park, Jong Sung, Alain-Beaudoin, Alexandra, Bériault, Silvain, Yu, Pengxin, Guo, Hongbin, Huang, Zhanyao, Li, Gengwan, Zhang, Xueru, Fan, Yubo, Liu, Han, Xin, Bowen, Nicolson, Aaron, Zhong, Lujia, Deng, Zhiwei, Müller-Franzes, Gustav, Khader, Firas, Li, Xia, Zhang, Ye, Hémon, Cédric, Boussot, Valentin, Zhang, Zhihao, Wang, Long, Bai, Lu, Wang, Shaobin, Mus, Derk, Kooiman, Bram, Sargeant, Chelsea A. H., Henderson, Edward G. A., Kondo, Satoshi, Kasai, Satoshi, Karimzadeh, Reza, Ibragimov, Bulat, Helfer, Thomas, Dafflon, Jessica, Chen, Zijie, Wang, Enpei, Perko, Zoltan, Maspero, Matteo

مصطلحات موضوعية: Physics - Medical Physics

الوصف: Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: 1) MRI-to-CT and 2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (>0.87/0.90) and gamma pass rates for photon (>98.1%/99.0%) and proton (>97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy.
Comment: Preprint submitted to Medical Image Analysis

الوصول الحر: http://arxiv.org/abs/2403.08447Test

المؤلفون: Zhu, Ruizhi, Zhang, Xinru, Pang, Haowen, Xu, Chundan, Ye, Chuyang

مصطلحات موضوعية: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning

الوصف: Synthesizing healthy brain scans from diseased brain scans offers a potential solution to address the limitations of general-purpose algorithms, such as tissue segmentation and brain extraction algorithms, which may not effectively handle diseased images. We consider this a 3D inpainting task and investigate the adaptation of 2D inpainting methods to meet the requirements of 3D magnetic resonance imaging(MRI) data. Our contributions encompass potential modifications tailored to MRI-specific needs, and we conducted evaluations of multiple inpainting techniques using the BraTS2023 Inpainting datasets to assess their efficacy and limitations.

الوصول الحر: http://arxiv.org/abs/2402.01509Test

المؤلفون: Liu, Wan, Ye, Chuyang

مصطلحات موضوعية: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition

الوصف: White matter (WM) tract segmentation is a crucial step for brain connectivity studies. It is performed on diffusion magnetic resonance imaging (dMRI), and deep neural networks (DNNs) have achieved promising segmentation accuracy. Existing DNN-based methods use an annotated dataset for model training. However, the performance of the trained model on a different test dataset may not be optimal due to distribution shift, and it is desirable to design WM tract segmentation approaches that allow better generalization of the segmentation model to arbitrary test datasets. In this work, we propose a WM tract segmentation approach that improves the generalization with scaled residual bootstrap. The difference between dMRI scans in training and test datasets is most noticeably caused by the different numbers of diffusion gradients and noise levels. Since both of them lead to different signal-to-noise ratios (SNRs) between the training and test data, we propose to augment the training scans by adjusting the noise magnitude and develop an adapted residual bootstrap strategy for the augmentation. To validate the proposed approach, two dMRI datasets were used, and the experimental results show that our method consistently improved the generalization of WM tract segmentation under various settings.

الوصول الحر: http://arxiv.org/abs/2309.13980Test

المؤلفون: Zhang, Xinru, Ou, Ni, Liu, Chenghao, Zhuo, Zhizheng, Liu, Yaou, Ye, Chuyang

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Automated brain tumor segmentation based on deep learning (DL) has achieved promising performance. However, it generally relies on annotated images for model training, which is not always feasible in clinical settings. Therefore, the development of unsupervised DL-based brain tumor segmentation approaches without expert annotations is desired. Motivated by the success of prompt learning (PL) in natural language processing, we propose an approach to unsupervised brain tumor segmentation by designing image-based prompts that allow indication of brain tumors, and this approach is dubbed as PL-based Brain Tumor Segmentation (PL-BTS). Specifically, instead of directly training a model for brain tumor segmentation with a large amount of annotated data, we seek to train a model that can answer the question: is a voxel in the input image associated with tumor-like hyper-/hypo-intensity? Such a model can be trained by artificially generating tumor-like hyper-/hypo-intensity on images without tumors with hand-crafted designs. Since the hand-crafted designs may be too simplistic to represent all kinds of real tumors, the trained model may overfit the simplistic hand-crafted task rather than actually answer the question of abnormality. To address this problem, we propose the use of a validation task, where we generate a different hand-crafted task to monitor overfitting. In addition, we propose PL-BTS+ that further improves PL-BTS by exploiting unannotated images with brain tumors. Compared with competing unsupervised methods, the proposed method has achieved marked improvements on both public and in-house datasets, and we have also demonstrated its possible extension to other brain lesion segmentation tasks.
Comment: Currently under review (from November 14th, 2022 until now)

الوصول الحر: http://arxiv.org/abs/2304.01472Test

المؤلفون: Liu, Wan, Chen, Yuqian, Ye, Chuyang, Makris, Nikos, Rathi, Yogesh, Cai, Weidong, Zhang, Fan, O'Donnell, Lauren J.

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence

الوصف: Neuroimaging measures of the brain's white matter connections can enable the prediction of non-imaging phenotypes, such as demographic and cognitive measures. Existing works have investigated traditional microstructure and connectivity measures from diffusion MRI tractography, without considering the shape of the connections reconstructed by tractography. In this paper, we investigate the potential of fiber tract shape features for predicting non-imaging phenotypes, both individually and in combination with traditional features. We focus on three basic shape features: length, diameter, and elongation. Two different prediction methods are used, including a traditional regression method and a deep-learning-based prediction method. Experiments use an efficient two-stage fusion strategy for prediction using microstructure, connectivity, and shape measures. To reduce predictive bias due to brain size, normalized shape features are also investigated. Experimental results on the Human Connectome Project (HCP) young adult dataset (n=1065) demonstrate that individual shape features are predictive of non-imaging phenotypes. When combined with microstructure and connectivity features, shape features significantly improve performance for predicting the cognitive score TPVT (NIH Toolbox picture vocabulary test). Overall, this study demonstrates that the shape of fiber tracts contains useful information for the description and study of the living human brain using machine learning.

الوصول الحر: http://arxiv.org/abs/2303.09124Test

المؤلفون: Liu, Wan, Lu, Qi, Zhuo, ZhiZheng, Liu, Yaou, Ye, Chuyang

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence

الوصف: Deep learning based methods have achieved state-of-the-art performance for automated white matter (WM) tract segmentation. In these methods, the segmentation model needs to be trained with a large number of manually annotated scans, which can be accumulated throughout time. When novel WM tracts, i.e., tracts not included in the existing annotated WM tracts, are to be segmented, additional annotations of these novel WM tracts need to be collected. Since tract annotation is time-consuming and costly, it is desirable to make only a few annotations of novel WM tracts for training the segmentation model, and previous work has addressed this problem by transferring the knowledge learned for segmenting existing WM tracts to the segmentation of novel WM tracts. However, accurate segmentation of novel WM tracts can still be challenging in the one-shot setting, where only one scan is annotated for the novel WM tracts. In this work, we explore the problem of one-shot segmentation of novel WM tracts. Since in the one-shot setting the annotated training data is extremely scarce, based on the existing knowledge transfer framework, we propose to further perform extensive data augmentation for the single annotated scan, where synthetic annotated training data is produced. We have designed several different strategies that mask out regions in the single annotated scan for data augmentation. Our method was evaluated on public and in-house datasets. The experimental results show that our method improves the accuracy of one-shot segmentation of novel WM tracts.

الوصول الحر: http://arxiv.org/abs/2303.06852Test

المؤلفون: Zhao, Zipei, Pang, Fengqian, Liu, Yaou, Liu, Zhiwen, Ye, Chuyang

المصدر: Machine.Learning.for.Biomedical.Imaging. 2 (2023)

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Cell detection in histopathology images is of great interest to clinical practice and research, and convolutional neural networks (CNNs) have achieved remarkable cell detection results. Typically, to train CNN-based cell detection models, every positive instance in the training images needs to be annotated, and instances that are not labeled as positive are considered negative samples. However, manual cell annotation is complicated due to the large number and diversity of cells, and it can be difficult to ensure the annotation of every positive instance. In many cases, only incomplete annotations are available, where some of the positive instances are annotated and the others are not, and the classification loss term for negative samples in typical network training becomes incorrect. In this work, to address this problem of incomplete annotations, we propose to reformulate the training of the detection network as a positive-unlabeled learning problem. Since the instances in unannotated regions can be either positive or negative, they have unknown labels. Using the samples with unknown labels and the positively labeled samples, we first derive an approximation of the classification loss term corresponding to negative samples for binary cell detection, and based on this approximation we further extend the proposed framework to multi-class cell detection. For evaluation, experiments were performed on four publicly available datasets. The experimental results show that our method improves the performance of cell detection in histopathology images given incomplete annotations for network training.
Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2022:027Test. arXiv admin note: text overlap with arXiv:2106.15918

الوصول الحر: http://arxiv.org/abs/2302.08050Test