Variational U-Net with Local Alignment for Joint Tumor Extraction and Registration (VALOR-Net) of Breast MRI Data Acquired at Two Different Field Strengths
University of Peshawar（白沙瓦大学）；Danube Private University（多瑙河私立大学）；University of Engineering & Technology, Peshawar（白沙瓦工程与技术大学）；Medical University of Vienna（维也纳医科大学）；Karl Landsteiner Society（卡尔·兰施泰纳学会）；University of Cambridge（剑桥大学）
Muhammad Shahkar Khan, Haider Ali, Laura Villazan Garcia, Noor Badshah, Siegfried Trattnig, Florian Schwarzhans, Ramona Woitek, Olgica Zaric
Background: Multiparametric breast MRI data might improve tumor diagnostics, characterization, and treatment planning. Accurate alignment and delineation of images acquired at different field strengths such as 3T and 7T, remain challenging research tasks. Purpose: To address alignment challenges and enable consistent tumor segmentation across different MRI field strengths. Study type: Retrospective. Subjects: Nine female subjects with breast tumors were involved: six histologically proven invasive ductal carcinomas (IDC) and three fibroadenomas. Field strength/sequence: Imaging was performed at 3T and 7T scanners using post-contrast T1-weighted three-dimensional time-resolved angiography with stochastic trajectories (TWIST) sequence. Assessments: The method's performance for joint image registration and tumor segmentation was evaluated using several quantitative metrics, including signal-to-noise ratio (PSNR), structural similarity index (SSIM), normalized cross-correlation (NCC), Dice coefficient, F1 score, and relative sum of squared differences (rel SSD). Statistical tests: The Pearson correlation coefficient was used to test the relationship between the registration and segmentation metrics. Results: When calculated for each subject individually, the PSNR was in a range from 27.5 to 34.5 dB, and the SSIM was from 82.6 to 92.8%. The model achieved an NCC from 96.4 to 99.3% and a Dice coefficient of 62.9 to 95.3%. The F1 score was between 55.4 and 93.2% and the rel SSD was in the range of 2.0 and 7.5%. The segmentation metrics Dice and F1 Score are highly correlated (0.995), while a moderate correlation between NCC and SSIM (0.681) was found for registration. Data conclusion: Initial results demonstrate that the proposed method may be feasible in providing joint tumor segmentation and registration of MRI data acquired at different field strengths.
Breast lesion localization using tactile imaging is a new and developing direction in medical science. To achieve the goal, proper image reconstruction and image registration can be a valuable asset. In this paper, a new approach of the segmentation-based image surface reconstruction algorithm is used to reconstruct the surface of a breast phantom. In breast tissue, the sub-dermal vein network is used as a distinguishable pattern for reconstruction. The proposed image capturing device contacts the surface of the phantom, and surface deformation will occur due to applied force at the time of sc…
Instituto Tecnológico y de Estudios Superiores de Monterrey（蒙特雷技术与高等教育学院）
Early tumor detection is key in reducing the number of breast cancer death and screening mammography is one of the most widely available and reliable method for early detection. However, it is difficult for the radiologist to process with the same attention each case, due the large amount of images to be read. Computer aided detection (CADe) systems improve tumor detection rate; but the current efficiency of these systems is not yet adequate and the correct interpretation of CADe outputs requires expert human intervention. Computer aided diagnosis systems (CADx) are being designed to improve c…
Digital Breast Tomosynthesis (DBT) provides an insight into the fine details of normal fibroglandular tissues and abnormal lesions by reconstructing a pseudo-3D image of the breast. In this respect, DBT overcomes a major limitation of conventional X-ray mammography by reducing the confounding effects caused by the superposition of breast tissue. In a breast cancer screening or diagnostic context, a radiologist is interested in detecting change, which might be indicative of malignant disease. To help automate this task image registration is required to establish spatial correspondence between t…
Muroran Institute of Technology（室兰工业大学）；Niigata University of Health and Welfare（新潟医疗福祉大学）；Konica Minolta, Inc.（柯尼卡美能达公司）
Multi-stain whole-slide-image (WSI) registration is an active field of research. It is unclear, however, how the current WSI registration methods would perform on a real-world data set. AutomatiC Registration Of Breast cAncer Tissue (ACROBAT) challenge is held to verify the performance of the current WSI registration methods by using a new dataset that originates from routine diagnostics to assess real-world applicability. In this report, we present our solution for the ACROBAT challenge. We employ a two-step approach including rigid and non-rigid transforms. The experimental results show that…
The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to manually assess status and scoring of several established biomarkers, including ER, PGR, HER2 and KI67. However, this is a task that can also be facilitated by computational pathology image analysis methods. The research in computational pathology has recently made numerous substantial advances, often …
Quantib-U（Quantib-U公司）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；University Medical Center Utrecht（乌得勒支大学医学中心）
Deep neural networks are increasingly used for pair-wise image registration. We propose to extend current learning-based image registration to allow simultaneous registration of multiple images. To achieve this, we build upon the pair-wise variational and diffeomorphic VoxelMorph approach and present a general mathematical framework that enables both registration of multiple images to their geodesic average and registration in which any of the available images can be used as a fixed image. In addition, we provide a likelihood based on normalized mutual information, a well-known image similarit…
University of Cambridge（剑桥大学）；Cancer Research UK Cambridge Centre（英国癌症研究中心剑桥中心）；Medical University Vienna（维也纳医科大学）；Cambridge University Hospitals National Health Service (NHS) Foundation Trust（剑桥大学医院国民保健服务基金会信托）；Eindhoven University of Technology（埃因霍温理工大学）；Leiden University（莱顿大学）；University College London（伦敦大学学院）；Cancer Research UK Cambridge Institute（英国癌症研究中心剑桥研究所）
This study investigates the use of the unsupervised deep learning framework VoxelMorph for deformable registration of longitudinal abdominopelvic CT images acquired in patients with bone metastases from breast cancer. The CT images were refined prior to registration by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of VoxelMorph when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images. In a 4-fold cross-validation scheme…
UWarp: A Whole Slide Image Registration Pipeline to Characterize Scanner-Induced Local Domain Shift
Ummon HealthTech SAS；Centre Georges-François Leclerc（乔治-弗朗索瓦·勒克莱尔中心）；ICMUB laboratory, Université de Bourgogne Europe（勃艮第欧洲大学ICMUB实验室）；University Hospital of Dijon（第戎大学医院）；Hospital de Tortosa Verge de la Cinta (HTVC), Catalan Health Institute (ICS)（托尔托萨维尔赫-德拉辛塔医院 (HTVC)，加泰罗尼亚卫生研究所 (ICS)）；Pere Virgili Institute for Health Research (IISPV)（佩雷·维尔吉利健康研究所 (IISPV)）；Universitat Rovira i Virgili (URV)（罗维拉-威尔吉利大学 (URV)）；Nałęcz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences（波兰科学院纳文奇生物控制论与生物医学工程研究所）；Cairo University（开罗大学）；University of Udine（乌迪内大学）
Diffeomorphic Multi-Resolution Deep Learning Registration for Applications in Breast MRI
Auckland Bioengineering Institute（奥克兰生物工程研究所）；University of Auckland（奥克兰大学）；Faculty of Medical and Health Sciences（医学与健康科学学院）；MGH/HST Martinos Center for Biomedical Imaging（麻省总医院/哈佛健康科学技术生物医学成像马蒂诺斯中心）；Harvard Medical School（哈佛医学院）；MIT（麻省理工学院）
Ecole Centrale de Nantes（南特中央理工学院）；Keosys Medical Imaging（Keosys医学影像公司）；University of Nantes（南特大学）；University of Angers（昂热大学）；ICO Cancer Center（ICO癌症中心）
Longitudinal image registration is challenging and has not yet benefited from major performance improvements thanks to deep-learning. Inspired by Deep Image Prior, this paper introduces a different use of deep architectures as regularizers to tackle the image registration question. We propose a subject-specific deformable registration method called MIRRBA, relying on a deep pyramidal architecture to be the prior parametric model constraining the deformation field. Diverging from the supervised learning paradigm, MIRRBA does not require a learning database, but only the pair of images to be reg…
GuidedMorph: Two-Stage Deformable Registration for Breast MRI
Department of Electrical and Computer Engineering, Duke University（电子工程系，杜克大学）；Department of Biostatistics & Bioinformatics, Duke University（生物统计学与生物信息学系，杜克大学）；Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida（健康结果与生物医学信息学系，医学院，佛罗里达大学）
University of Oklahoma（俄克拉荷马大学）；XPeng Motors（小鹏汽车）
Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operation, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying or mapping corresponding breast lesion features computed from unregistered multiple mammograms. This motivates us to leverage the architecture of Multi-view Vision Transformers to capture long-range relationships of multiple mammograms from the same patient in one examination. For this purpose, we employ local Transformer blocks t…
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
Radboud University Medical Centre（拉德堡德大学医学中心）；The Netherlands Cancer Institute（荷兰癌症研究所）；Macao Polytechnic University（澳门理工大学）；Maastricht University Medical Centre（马斯特里赫特大学医学中心）
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
Karolinska Institutet（卡罗林斯卡学院）；University of Turku（图尔库大学）；University of Eastern Finland（东芬兰大学）；Zealand University Hospital（西兰大学医院）；Gestalt Diagnostics（格式塔诊断公司）；Moffitt Cancer Center（莫菲特癌症中心）；University of Applied Sciences Western Switzerland（瑞士西部应用科学大学）；AGH University of Kraków（克拉科夫AGH科技大学）；University of Geneva（日内瓦大学）；University of Padova（帕多瓦大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；University of Twente（特温特大学）；Muroran Institute of Technology（室兰工业大学）；Niigata University of Health and Welfare（新潟医疗福祉大学）；Konica Minolta, Inc.（柯尼卡美能达株式会社）；University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）；Karolinska University Hospital（卡罗林斯卡大学医院）；Tampere University（坦佩雷大学）
Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
Department of Electrical and Computer Engineering, Duke University（电子工程与计算机科学系，杜克大学）；Department of Biostatistics and Bioinformatics, Duke University（生物统计学与生物信息学系，杜克大学）；Departments of Biostatistics and Bioinformatics, Radiology, Electrical and Computer Engineering, and Computer Science, Duke University（生物统计学与生物信息学系、放射学、电子工程与计算机科学系，杜克大学）
Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
Department of Physics and Technology, UiT The Arctic University of Norway（物理与技术系，UiT北极大学）；SPKI The Norwegian Centre for Clinical Artificial Intelligence（挪威临床人工智能中心）；Department of Computer Science and Engineering, University of Bologna（计算机科学与工程系，博洛尼亚大学）；Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering IMTE（个体化和细胞基础医学工程研究机构IMTE）；Norwegian Computing Center, Oslo, Norway（挪威计算中心，奥斯陆，挪威）；Pioneer Centre for AI, University of Copenhagen（人工智能先锋中心，哥本哈根大学）
