Article
Engineering, Biomedical
Xu Zhang, Bin Zhang, Shengming Deng, Qingquan Meng, Xinjian Chen, Dehui Xiang
Summary: This paper introduces a novel network for lung tumor segmentation. The network is able to simultaneously fuse PET and CT images, extract their features, and solve the problem of blurred boundaries. Experimental results demonstrate that the proposed method achieves high segmentation accuracy on PET-CT images of non-small cell lung cancer.
PHYSICS IN MEDICINE AND BIOLOGY
(2022)
Article
Biochemical Research Methods
Jitao Li, Zongjin Qu, Yue Yang, Fuchun Zhang, Meng Li, Shunbo Hu
Summary: In this study, we propose a generator based on a combination of transformer network and CNN for multimodal medical image synthesis, achieving better results.
BIOMEDICAL OPTICS EXPRESS
(2022)
Article
Computer Science, Information Systems
Dehui Xiang, Bin Zhang, Yuxuan Lu, Shengming Deng
Summary: Segmentation of lung tumors in PET-CT images is a challenging task in medical image processing. To address this issue, a modality-specific segmentation network (MoSNet) is proposed, which can simultaneously segment the lung tumor in PET images and CT images.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Lei Bi, Michael Fulham, Nan Li, Qiufang Liu, Shaoli Song, David Dagan Feng, Jinman Kim
Summary: A recurrent fusion network (RFN) is proposed in this study to progressively fuse multi-modality image features through multiple recurrent fusion phases, producing consistent segmentation results across different network architectures. The RFN method shows more accurate segmentation compared to existing methods and is generalizable to different datasets.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Computer Science, Software Engineering
Sebastian Weiss, Ruediger Westermann
Summary: This paper presents a differentiable volume rendering solution that allows for differentiation of all parameters of the rendering process. The approach is tailored for volume rendering and facilitates automatic optimization of parameters and volumetric density field. The effectiveness of the method is demonstrated through experiments and comparisons with other techniques.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Jennifer P. Kieselmann, Clifton D. Fuller, Oliver J. Gurney-Champion, Uwe Oelfke
Summary: This study developed a method using annotated CT images to generate synthetic MR images for training a CNN model to segment parotid glands on MR images of head and neck cancer patients. The accuracy of the segmentation was close to interobserver variation and segmentation accuracy of CT images, showing potential for solving segmentation problems with sparse training data.
Article
Automation & Control Systems
Muhammad Zubair Islam, Rizwan Ali Naqvi, Amir Haider, Hyung Seok Kim
Summary: Tumor lesion segmentation and staging in cancer patients are challenging tasks, and DL-based tumor auto-segmentation methods have been developed to address the challenges of PET/CT multi-modality image-based tumor segmentation. This survey paper explores the weaknesses of PET and CT, the challenges of PET/CT images, and evaluates the achievements and limitations of existing auto-segmentation methods. It also classifies the methods based on their model architecture design and discusses solutions to improve segmentation performance.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Biomedical
Elin Tornquist, Sophie Le Cann, Erika Tudisco, Alessandro Tengattini, Edward Ando, Nicolas Lenoir, Johan Hektor, Deepak Bushan Raina, Magnus Tagil, Stephen A. Hall, Hanna Isaksson
Summary: This study compares the visualization and contrast-to-noise ratio of neutron and x-ray tomography in imaging bone and metallic implants, and highlights the benefits of combining both modalities through joint histogram analysis. The results demonstrate the differences in how neutrons and x-rays interact with tissues and implants, offering insights for future refinement of segmentation techniques and obtaining novel specimen-specific information.
PHYSICS IN MEDICINE AND BIOLOGY
(2021)
Article
Computer Science, Interdisciplinary Applications
Xu Chen, Chunfeng Lian, Li Wang, Hannah Deng, Tianshu Kuang, Steve Fung, Jaime Gateno, Pew-Thian Yap, James J. Xia, Dinggang Shen
Summary: An anatomy-regularized representation learning approach is proposed for segmentation-oriented cross-modality image synthesis, showing superiority in comparison with state-of-the-art cross-modality medical image segmentation methods.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Engineering, Biomedical
Guoyu Tong, Huiyan Jiang
Summary: Cancer is a major cause of death, and PET/CT imaging is used to detect tumors. However, many tumors are only obvious in one modality, and PET contains non-lesional hypermetabolic regions, making segmentation difficult. To address this, we propose a network guided by soft segmentation for tumor segmentation on PET/CT images.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Jiadong Zhang, Zhiming Cui, Caiwen Jiang, Shanshan Guo, Fei Gao, Dinggang Shen
Summary: This article proposes a learning-based method to reconstruct high-dose positron emission tomography (PET) images from low-dose PET images and corresponding total-body computed tomography (CT) images. The proposed hierarchical framework can consistently improve the performance of all body parts and outperforms the state-of-the-art methods in single-photon emission computed tomography (SPET) image reconstruction, with a peak signal-to-noise ratio (PSNR) of 30.6 dB for total-body PET images.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Yixin Wang, Yao Zhang, Yang Liu, Jiang Tian, Cheng Zhong, Zhongchao Shi, Yang Zhang, Zhiqiang He
Summary: This study introduces a hybrid encoder learning method based on a multi-lesion pre-trained model, which achieves better training performance for COVID-19 infection segmentation. By utilizing multiple lung lesion datasets and transfer learning strategies, the model demonstrates improved accuracy and generalization, showing the effectiveness of incorporating non-COVID-19 lung lesion features for COVID-19 CT image segmentation.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Engineering, Biomedical
Zhaofeng Chen, Tianshuang Qiu, Yang Tian, Hongbo Feng, Yanjun Zhang, Hongkai Wang
Summary: The study introduces a PET/CT-based brain VOI segmentation algorithm that combines anatomical atlas, local landmarks, and dual-modality information. By incorporating local deep brain landmarks and dual-modality PET/CT image information, the algorithm improves registration accuracy and achieves accurate delineation of brain VOIs.
PHYSICS IN MEDICINE AND BIOLOGY
(2021)
Article
Computer Science, Information Systems
Xin Fan, Junyan Wang, Haifeng Wang, Changgao Xia
Summary: A histogram-constrained and contrast-tunable HE technique for digital image enhancement is proposed in this paper, which partitions the input image histogram into two parts and redistributes them to achieve more accurate results in terms of information entropy and MS-SSIM compared to other algorithms.
Article
Computer Science, Interdisciplinary Applications
Yaoting Yue, Nan Li, Gaobo Zhang, Zhibin Zhu, Xin Liu, Shaoli Song, Dean Ta
Summary: In this study, a deep network called GloD-LoATUNet was designed to accurately delineate the tumor volume of esophageal squamous cell carcinoma on medical images. By integrating global deformable dense attention transformer, local attention transformer, and convolution blocks, the network demonstrated remarkable representation learning capabilities and performed well in predicting the small and variable esophageal tumor volume. The proposed approach was validated in clinical practice and showed consistent results with the ground truth.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Letter
Clinical Neurology
Shadi El-Wahsh, David Greenup, Gemma White, Elizabeth O. Thompson, Arun Aggarwal, Michael J. Fulham, Gabor Michael Halmagyi
JOURNAL OF NEUROLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Lei Bi, Michael Fulham, Jinman Kim
Summary: Segmentation of skin lesions using fully convolutional networks is accurate but limited by insufficient training data. Semi-automatic segmentation methods that combine user-inputs with high-level image features offer a better solution for challenging skin lesions. The proposed hyper-fusion network (HFN) in this study outperformed state-of-the-art methods in accuracy and generalizability.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Multidisciplinary Sciences
Tian Xia, Ashnil Kumar, Michael Fulham, Dagan Feng, Yue Wang, Eun Young Kim, Younhyun Jung, Jinman Kim
Summary: Radiogenomics relationships aim to identify correlations between medical image features and molecular characteristics. Traditional studies have relied on single image feature extraction techniques, while this study proposes a fused feature signature approach that combines handcrafted and deep learning techniques to better represent these relationships. Experimental results show that this approach can accurately represent tumor imaging characteristics and identify more relationships related to important biological functions, suggesting its potential for cancer diagnosis and treatment.
SCIENTIFIC REPORTS
(2022)
Article
Clinical Neurology
Patrick H. Luckett, Charlie Chen, Brian A. Gordon, Julie Wisch, Sarah B. Berman, Jasmeer P. Chhatwal, Carlos Cruchaga, Anne M. Fagan, Martin R. Farlow, Nick C. Fox, Mathias Jucker, Johannes Levin, Colin L. Masters, Hiroshi Mori, James M. Noble, Stephen Salloway, Peter R. Schofield, Adam M. Brickman, William S. Brooks, David M. Cash, Michael J. Fulham, Bernardino Ghetti, Clifford R. Jack, Jonathan Voeglein, William E. Klunk, Robert Koeppe, Yi Su, Michael Weiner, Qing Wang, Daniel Marcus, Deborah Koudelis, Nelly Joseph-Mathurin, Lisa Cash, Russ Hornbeck, Chengjie Xiong, Richard J. Perrin, Celeste M. Karch, Jason Hassenstab, Eric McDade, John C. Morris, Tammie L. S. Benzinger, Randall J. Bateman, Beau M. Ances
Summary: This study analyzed 19 biomarkers of Alzheimer's disease using hierarchical clustering and feature selection, and found that amyloid and tau measures were the primary predictors. Emerging biomarkers of neuronal integrity and inflammation showed weaker predictive ability.
ALZHEIMERS & DEMENTIA
(2023)
Article
Biochemical Research Methods
Melissa E. Rodnick, Carina Sollert, Daniela Stark, Mara Clark, Andrew Katsifis, Brian G. Hockley, D. Christian Parr, Jens Frigell, Bradford D. Henderson, Laura Bruton, Sean Preshlock, Monica Abghari-Gerst, Morand R. Piert, Michael J. Fulham, Stefan Eberl, Katherine Gagnon, Peter J. H. Scott
Summary: [Ga-68]Ga-PSMA-11, a radiopharmaceutical approved for prostate cancer PET imaging, is in high demand. This study presents synthesis methods for [Ga-68]Ga-PSMA-11 using generator-eluted and cyclotron-produced Ga-68. Both methods are suitable for clinical production, but the cyclotron method is more promising for meeting high patient volumes in the long term.
Editorial Material
Clinical Neurology
Sophie Dunkerton, Ross Penninkilampi, Heidi Beadnall, Michael Fulham, Andrew Colebatch, Stacey Jankelowitz, Rebekah Ahmed, Zoe Thayer, Michael Halmagyi, Edward Abadir
PRACTICAL NEUROLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Xiaohang Fu, Lei Bi, Ashnil Kumar, Michael Fulham, Jinman Kim
Summary: The identification of melanoma can be done through the analysis of clinical and dermoscopy images. Current methods lack the ability to fully utilize information from both modalities and exploit the intercategory relationships in the 7PC. This study proposes a graph-based network with two modules to address these limitations and improves classification performance.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Biology
Xiaohang Fu, Ellis Patrick, Jean Y. H. Yang, David Dagan Feng, Jinman Kim
Summary: The spatial architecture and phenotypic heterogeneity of tumor cells are associated with cancer prognosis and outcomes. Imaging mass cytometry captures high-dimensional maps of disease-relevant biomarkers at single-cell resolution, which can inform patient-specific prognosis. However, existing methods for survival prediction do not utilize spatial phenotype information at the single-cell level, and there is a lack of end-to-end methods that integrate imaging data with clinical information for improved accuracy. We propose a deep multimodal graph-based network that considers spatial phenotype information and clinical variables to enhance survival prediction, and demonstrate its effectiveness in breast cancer datasets.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Information Systems
Usman Naseem, Matloob Khushi, Jinman Kim
Summary: Pathology visual question answering (PathVQA) aims to answer medical questions using pathology images. Existing methods have limitations in capturing the high and low-level interactions between vision and language features required for VQA. Additionally, these methods lack interpretability in justifying the retrieved answers. To address these limitations, a vision-language transformer called TraP-VQA is introduced, which embeds vision and language features for interpretable PathVQA. Our experiments demonstrate that TraP-VQA outperforms state-of-the-art methods and validate its robustness on medical VQA datasets, along with the capability of the integrated vision-language model. Visualization results explain the reasoning behind the retrieved PathVQA answers.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Automation & Control Systems
Yuyu Guo, Lei Bi, Dongming Wei, Liyun Chen, Zhengbin Zhu, Dagan Feng, Ruiyan Zhang, Qian Wang, Jinman Kim
Summary: In this study, we propose a dense-sparse-dense (DSD) motion estimation framework that utilizes unsupervised 3D landmark detection network and motion reconstruction network to extract sparse landmarks and construct motion field in two stages. The method improves the accuracy of motion estimation and preserves anatomical topology.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Letter
Hematology
Judith Trotman, Peter Presgrave, Duncan P. Carradice, Douglas Stuart Lenton, Maher K. Gandhi, Tara Cochrane, Xavier Badoux, Julia Carlson, Gloria Nkhoma, Belinda Butcher, Armin Nikpour, Michael Fulham, Anna M. Johnston
Article
Computer Science, Interdisciplinary Applications
Wenxiang Ding, Qiaoqiao Ding, Kewei Chen, Miao Zhang, Li Lv, David Dagan Feng, Lei Bi, Jinman Kim, Qiu Huang
Summary: Dynamic PET imaging provides more comprehensive physiological information than conventional static PET imaging. The proposed modified Logan reference plot model and self-supervised convolutional neural network improve noise performance and accurately estimate the distribution volume ratio in dynamic PET with a shortened scanning protocol. The method has the potential to add clinical value by providing both DVR and SUV simultaneously.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Computer Science, Cybernetics
Usman Naseem, Matloob Khushi, Jinman Kim, Adam G. Dunn
Summary: People on social media using disease and symptom words to discuss their health can introduce biases in data-driven public health applications. This study presents a new dataset called RHMD, which consists of 10,015 manually annotated Reddit posts. The dataset is labeled with four categories and provides a comprehensive performance analysis of baseline methods. The release of this dataset is expected to facilitate the development of new methods for detecting health mentions in user-generated text.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Review
Automation & Control Systems
Wei-Chien Wang, Euijoon Ahn, Dagan Feng, Jinman Kim
Summary: Over the last decade, supervised deep learning has made significant progress in computer vision tasks using manually annotated big data. However, the limited availability of high-quality annotated medical imaging data hinders the application of deep learning in medical image analysis. A potential solution is the use of self-supervised learning (SSL), particularly contrastive SSL, which has shown promise in rivaling or surpassing supervised learning. This review examines state-of-the-art contrastive SSL algorithms originally designed for natural images, explores their adaptations for medical images, and discusses recent advances, current limitations, and future directions in applying contrastive SSL in the medical domain.
MACHINE INTELLIGENCE RESEARCH
(2023)
Article
Biochemistry & Molecular Biology
Hendris Wongso, Maiko Ono, Tomoteru Yamasaki, Katsushi Kumata, Makoto Higuchi, Ming-Rong Zhang, Michael J. Fulham, Andrew Katsifis, Paul A. Keller
Summary: The pyridinyl-butadienyl-benzothiazole (PBB3 15) scaffold was used to improve tau ligands for imaging Alzheimer's disease. Triazole derivatives visualized A beta plaques but failed to detect neurofibrillary tangles (NFTs), while amide 110 and ester 129 successfully observed NFTs. These ligands showed different affinities at the binding sites with PBB3.
RSC MEDICINAL CHEMISTRY
(2023)