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Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks

Zeitschrift fur Medizinische Physik 2024年

作者： Izadi, Saeed Shiri, Isaac F. Uribe, Carlos Geramifar, Parham Zaidi, Habib Rahmim, Arman Hamarneh, Ghassan Medical Image Analysis Lab School of Computing Science Simon Fraser University Canada Division of Nuclear Medicine and Molecular Imaging Geneva University Hospital Geneva 4 GenevaCH-1211 Switzerland Department of Cardiology Inselspital Bern University Hospital University of Bern Bern Switzerland Department of Integrative Oncology BC Cancer Research Institute Vancouver Canada Department of Radiology University of British Columbia Vancouver Canada Molecular Imaging and Therapy BC Cancer VancouverBC Canada Research Center for Nuclear Medicine Tehran University of Medical Sciences Tehran Iran Department of Nuclear Medicine and Molecular Imaging University of Groningen University Medical Center Groningen Groningen Netherlands Department of Nuclear Medicine University of Southern Denmark Odense Denmark University Research and Innovation Center Óbuda University Budapest Hungary Department of Physics and Astronomy University of British Columbia Vancouver Canada

In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of 14.30±3.88% and a relative error of -2

关键词： Positron emission tomography

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Temporal Graph Representation Learning for Autism spectrum disorder Brain Networks

Temporal Graph Representation Learning for Autism spectrum d...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Peng Cao Guangqi Wen Lanting Li Xiaoli Liu Jinzhu Yang Osmar Zaiane College of Computer Science and Engineering Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang China College of Computer Science and Engineering Northeastern University Shenyang China A.I. Lab Alibaba Hangzhou China Amii University of Alberta Edmonton Canada

ISBN: (纸本)9781665429825

Modeling spatio-temporal dynamics in functional brain networks is critical for underlying the functional mechanism of autism spectrum disorder (ASD). In our study, we propose an end-to-end framework called temporal graph representation learning for brain networks, which thoroughly captures spatio-temporal features in resting-state functional magnetic resonance imaging (rs-fMRI) data. Specifically, we first transform rs-fMRI time-series into temporal multi-graph using a sliding window technique. A temporal multi-graph clustering is then designed to eliminate the inconsistency of the temporal multi-graph series. Then, a graph structure aware LSTM (GSA-LSTM) is proposed to capture the spatio-temporal embedding for temporal graphs. The proposed GSA-LSTM can not only capture discriminative features for prediction but also impute the incomplete graphs for the temporal multi-graph series. Extensive experiments on autism brain imaging data exchange (ABIDE) dataset shows the effectiveness of our proposed framework. The results demonstrate that the proposed dynamic brain network embedding learning outperforms the state of-the-art brain network classification models. Furthermore, the obtained clustering results are consistent with the previous neuroimaging-derived evidence of biomarkers for autism spectrum disorder (ASD).

关键词： Representation learning Neurological diseases Neuroimaging Autism Biological system modeling Magnetic resonance Transforms

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Generalize Ultrasound image Segmentation Via Instant And Plug & Play Style Transfer

Generalize Ultrasound Image Segmentation Via Instant And Plu...

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IEEE International Symposium on Biomedical Imaging

作者： Zhendong Liu Xiaoqiong Huang Xin Yang Rui Gao Rui Li Yuanji Zhang Yankai Huang Guangquan Zhou Yi Xiong Alejandro F Frangi Dong Ni School of Biomedical Engineering Health Science Center Shenzhen University China Medical UltraSound Image Computing (MUSIC) Lab Shenzhen University China Luohu People’s Hospital Shenzhen China School of Biological Sciences and Medical Engineering Southeast University China Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) School of Computing University of Leeds Leeds UK Medical Imaging Research Center (MIRC) University Hospital Gasthuisberg KU Leuven Leuven Belgium

Deep segmentation models that generalize to images with unknown appearance are important for real-world medical image analysis. Retraining models leads to high latency and complex pipelines, which are impractical in clinical settings. The situation becomes more severe for ultrasound image analysis because of their large appearance shifts. In this paper, we propose a novel method for robust segmentation under unknown appearance shifts. Our contribution is three-fold. First, we advance a one-stage plug-and-play solution by embedding hierarchical style transfer units into a segmentation architecture. Our solution can remove appearance shifts and perform segmentation simultaneously. Second, we adopt Dynamic Instance Normalization to conduct precise and dynamic style transfer in a learnable manner, rather than previously fixed style normalization. Third, our solution is fast and lightweight for routine clinical adoption. Given 400×400 image input, our solution only needs an additional 0.2 ms and 1.92M FLOPs to handle appearance shifts compared to the baseline pipeline. Extensive experiments are conducted on a large dataset from three vendors demonstrate our proposed method enhances the robustness of deep segmentation models.

关键词： image segmentation Analytical models Ultrasonic imaging image analysis Biological system modeling Pipelines Robustness

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Synthesis and Edition of Ultrasound images via Sketch Guided Progressive Growing GANS

Synthesis and Edition of Ultrasound Images via Sketch Guided...

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IEEE International Symposium on Biomedical Imaging

作者： Jiamin Liang Xin Yang Haoming Li Yi Wang Manh The Van Haoran Dou Chaoyu Chen Jinghui Fang Xiaowen Liang Zixin Mai Guowen Zhu Zhiyi Chen Dong Ni Medical UltraSound Image Computing (MUSIC) Lab Shenzhen University Shenzhen China Experimental Center of Liwan Hospital The Third Affiliated Hospital of Guangzhou Medical University Guangzhou China

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

Ultrasound (US) is widely accepted in clinic for anatomical structure inspection. However, lacking in resources to practice US scan, novices often struggle to learn the operation skills. Also, in the deep learning era, automated US image analysis is limited by the lack of annotated samples. Efficiently synthesizing realistic, editable and high resolution US images can solve the problems. The task is challenging and previous methods can only partially complete it. In this paper, we devise a new framework for US image synthesis. Particularly, we firstly adopt a sketch generative adversarial networks (Sgan) to introduce background sketch upon object mask in a conditioned generative adversarial network. With enriched sketch cues, Sgan can generate realistic US images with editable and fine-grained structure details. Although effective, Sgan is hard to generate high resolution US images. To achieve this, we further implant the Sgan into a progressive growing scheme (PGSgan). By smoothly growing both generator and discriminator, PGSgan can gradually synthesize US images from low to high resolution. By synthesizing ovary and follicle US images, our extensive perceptual evaluation, user study and segmentation results prove the promising efficacy and efficiency of the proposed PGSgan.

关键词： Training Generators image resolution image generation Ultrasonic imaging Gallium nitride image segmentation

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Continuous-time deep glioma growth models

arXiv

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arXiv 2021年

作者： Petersen, Jens Isensee, Fabian Köhler, Gregor Jäger, Paul F. Zimmerer, David Neuberger, Ulf Wick, Wolfgang Debus, Jürgen Heiland, Sabine Bendszus, Martin Vollmuth, Philipp Maier-Hein, Klaus H. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIP Applied Computer Vision Lab Division of Medical Image Computing German Cancer Research Center Germany Interactive Machine Learning Group German Cancer Research Center Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Neurology Clinic Heidelberg University Hospital Germany DKTK CCU Neurooncology German Cancer Research Center Germany Heidelberg Germany Heidelberg University Hospital Germany Clinical Cooperation Unit Radiation Oncology German Cancer Research Center Germany

The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy. Recent work has approached the glioma growth modeling problem via deep learning and variational inference, thus learning growth dynamics entirely from a real patient data distribution. So far, this approach was constrained to predefined image acquisition intervals and sequences of fixed length, which limits its applicability in more realistic scenarios. We overcome these limitations by extending Neural Processes, a class of conditional generative models for stochastic time series, with a hierarchical multi-scale representation encoding including a spatio-temporal attention mechanism. The result is a learned growth model that can be conditioned on an arbitrary number of observations, and that can produce a distribution of temporally consistent growth trajectories on a continuous time axis. On a dataset of 379 patients, the approach successfully captures both global and finer-grained variations in the images, exhibiting superior performance compared to other learned growth models. © 2021, CC BY-SA.

关键词： Tumors

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Effects of anterior temporal lobe resection on cortical morphology

arXiv

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arXiv 2022年

作者： Leiberg, Karoline de Tisi, Jane Duncan, John S. Little, Bethany Taylor, Peter N. Vos, Sjoerd B. Winston, Gavin P. Mota, Bruno Wang, Yujiang CNNP Lab Interdisciplinary Computing and Complex BioSystems Group School of Computing Newcastle University Newcastle upon Tyne United Kingdom Department of Clinical & Experimental Epilepsy UCL Queen Square Institute of Neurology London United Kingdom Chalfont Centre for Epilepsy Chalfont St Peter United Kingdom Faculty of Medical Sciences Newcastle University Newcastle upon Tyne United Kingdom Queen Square Institute of Neurology University College London Queen Square London United Kingdom Neuroradiological Academic Unit Department of Brain Repair and Rehabilitation UCL United Kingdom Centre for Medical Image Computing University College London London United Kingdom MRI Unit Epilepsy Society Buckinghamshire United Kingdom Division of Neurology Department of Medicine Queen’s University KingstonON Canada Rio de Janeiro Brazil

Anterior temporal lobe resection (ATLR) is a surgical procedure to treat drug-resistant temporal lobe epilepsy (TLE). Resection may involve large amounts of cortical tissue. Here, we examine the effects of this surgery on cortical morphology measured in independent variables both near the resection and remotely. We studied 101 individuals with TLE (55 left, 46 right onset) who underwent ATLR. For each individual we considered one pre-surgical MRI and one follow-up MRI 2 to 13 months after surgery. We used our newly developed surface-based method to locally compute traditional morphological variables (average cortical thickness, exposed surface area, and total surface area), and the independent measures K, I, and S, where K measures white matter tension, I captures isometric scaling, and S contains the remaining information about cortical shape. Data from 924 healthy controls was included to account for healthy ageing effects occurring during scans. A SurfStat random field theory clustering approach assessed changes across the cortex caused by ATLR. Compared to preoperative data, surgery had marked effects on all morphological measures. Ipsilateral effects were located in the orbitofrontal and inferior frontal gyri, the pre- and postcentral gyri and supramarginal gyrus, and the lateral occipital gyrus and lingual cortex. Contralateral effects were in the lateral occipital gyrus, and inferior frontal gyrus and frontal pole. The restructuring following ATLR is reflected in widespread morphological changes, mainly in regions near the resection, but also remotely in regions that are structurally connected to the anterior temporal lobe. The causes could include mechanical effects, Wallerian degeneration, or compensatory plasticity. The study of independent measures revealed additional effects compared to traditional measures. © 2022, CC BY.

关键词： Morphology

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Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images

arXiv

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arXiv 2021年

作者： Casamitjana, Adrià Mancini, Matteo Iglesias, Juan Eugenio Center for Medical Image Computing University College London United Kingdom Department of Neuroscience University of Sussex Brighton United Kingdom NeuroPoly Lab Polytechnique Montreal Canada CUBRIC Cardiff University United Kingdom Martinos Center for Biomedical Imaging MGH Harvard Medical School United States Computer Science and AI Laboratory Massachusetts Institute of Technology United States

Nonlinear inter-modality registration is often challenging due to the lack of objective functions that are good proxies for alignment. Here we propose a synthesis-by-registration method to convert this problem into an easier intra-modality task. We introduce a registration loss for weakly supervised image translation between domains that does not require perfectly aligned training data. This loss capitalises on a registration U-Net with frozen weights, to drive a synthesis CNN towards the desired translation. We complement this loss with a structure preserving constraint based on contrastive learning, which prevents blurring and content shifts due to overfitting. We apply this method to the registration of histological sections to MRI slices, a key step in 3D histology reconstruction. Results on two public datasets show improvements over registration based on mutual information (13% reduction in landmark error) and synthesis-based algorithms such as CycleGAN (11% reduction), and are comparable to registration with label supervision. Code and data are publicly available at https://***/acasamitjana/SynthByReg. © 2021, CC BY.

关键词： Digital storage

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nnDetection: A Self-configuring Method for medical Object Detection

arXiv

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arXiv 2021年

作者： Baumgartner, Michael Jäger, Paul F. Isensee, Fabian Maier-Hein, Klaus H. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Interactive Machine Learning Group German Cancer Research Center Germany HIP Applied Computer Vision Lab. German Cancer Research Center Germany Pattern Analysis and Learning Group Heidelberg University Hospital Germany

Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rather than e.g. pixels. For this task, the cumbersome and iterative process of method configuration constitutes a major research bottleneck. Recently, nnU-Net has tackled this challenge for the task of image segmentation with great success. Following nnU-Net’s agenda, in this work we systematize and automate the configuration process for medical object detection. The resulting self-configuring method, nnDetection, adapts itself without any manual intervention to arbitrary medical detection problems while achieving results en par with or superior to the state-of-the-art. We demonstrate the effectiveness of nnDetection on two public benchmarks, ADAM and LUNA16, and propose 11 further medical object detection tasks on public data sets for comprehensive method evaluation. Code is at https://***/MIC-DKFZ/nnDetection. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

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Automatic plane adjustment of orthopedic intra-operative flat panel detector CT-volumes

arXiv

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arXiv 2021年

作者： Vicario, Celia Martín Kordon, Florian Denzinger, Felix El Barbari, Jan Siad Privalov, Maxim Franke, Jochen Thomas, Sarina Kausch, Lisa Maier, Andreas Kunze, Holger Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Erlangen Germany Siemens Healthcare GmbH Forchheim Germany Department for Trauma and Orthopaedic Surgery BG Trauma Center Ludwigshafen Ludwigshafen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany

Purpose 3D acquisitions are often acquired to assess the result in orthopedic trauma surgery. With a mobile C-Arm system, these acquisitions can be performed intra-operatively. That reduces the number of required revision surgeries. However, due to the operation room setup, the acquisitions typically cannot be performed such that the acquired volumes are aligned to the anatomical regions. Thus, the multiplanar reconstructed (MPR) planes need to be adjusted manually during the review of the volume. In this paper, we present a detailed study of multi-task learning (MTL) regression networks to estimate the parameters of the MPR planes. Approach First, various mathematical descriptions for rotation, including Euler angle, quaternion, and matrix representation, are revised. Then, three different MTL network architectures based on the PoseNet are compared with a single task learning network. Results Using a matrix description rather than the Euler angle description, the accuracy of the regressed normals improves from 7.7◦ to 7.3◦ in the mean value for single anatomies. The multi-head approach improves the regression of the plane position from 7.4mm to 6.1mm, while the orientation does not benefit from this approach. Conclusions The results show that a multi-head approach can lead to slightly better results than the individual tasks networks. The most important benefit of the MTL approach is that it is a single network for standard plane regression for all body regions with a reduced number of stored parameters. Copyright © 2021, The Authors. All rights reserved.

关键词： Learning systems

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： image segmentation

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