Article
Cardiac & Cardiovascular Systems
Giulio Falasconi, Diego Penela, David Soto-Iglesias, Pietro Francia, Cheryl Teres, Andrea Saglietto, Beatriz Jauregui, Daniel Viveros, Aldo Bellido, Jose Alderete, Julia Meca-Santamaria, Paula Franco, Carlo Gaspardone, Rodolfo San Antonio, Marina Huguet, Oscar Camara, Jose-Tomas Ortiz-Perez, Julio Marti-Almor, Antonio Berruezo
Summary: The study aimed to personalize the ablation index (AI) based on left atrial wall thickness (LAWT) and investigate its effectiveness in PV antrum isolation for persistent atrial fibrillation (PeAF). The results showed that this personalized approach was effective and efficient, with low procedure, fluoroscopy, and RF time. The importance of this study lies in providing a new method for the treatment of PeAF.
Article
Biology
Mattia Corti, Alberto Zingaro, Luca Dede', Alfio Maria Quarteroni
Summary: This study analyzes the hemodynamics of the left atrium, comparing differences between healthy individuals and patients with atrial fibrillation. Using patient-specific geometries, a computational simulation of blood flow dynamics in the left atria is conducted. A novel procedure for computing boundary data for 3D hemodynamic simulations is introduced, which is particularly helpful in the absence of clinical measurements. Various fluid dynamics indicators are evaluated for atrial hemodynamics and validated against clinical measurements. The impact of geometric and clinical characteristics on the risk of thrombosis is investigated, and a new indicator called "age stasis" is proposed to highlight the correlation between thrombus formation and atrial fibrillation.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Medicine, General & Internal
Melanie A. Gunawardene, Gerrit Frommeyer, Christian Ellermann, Mario Jularic, Patrick Leitz, Jens Hartmann, Philipp Sebastian Lange, Omar Anwar, Benjamin Rath, Rahin Wahedi, Lars Eckardt, Stephan Willems
Summary: This study aimed to investigate the efficacy and safety of left atrial posterior wall isolation (LAPWI) performed by non-thermal pulsed field ablation (PFA) in catheter ablation (CA) for persistent atrial fibrillation (persAF). The results showed that PFA-guided LAPWI is feasible and safe in patients undergoing CA for persAF and shows favorable outcomes.
JOURNAL OF CLINICAL MEDICINE
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Xin Tian, Cen Wang, Duo Gao, Bu-Lang Gao, Cai-Ying Li
Summary: This study aimed to assess the morphological and functional features of the left atrium (LA) and the left atrial appendage (LAA) in patients with atrial fibrillation (AF) using computed tomography angiography (CTA) images. The results showed that AF patients had a larger minor axis of the LAA orifice and a more circular LAA orifice compared to the control group. The LAA orifice area and perimeter were positively correlated with LAA volume change. Female patients had larger LAA orifice major and minor axes, area, perimeter, and LAA depth compared to male patients in the AF group.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2022)
Article
Medicine, General & Internal
Mireia Ble, Begona Benito, Elisa Cuadrado-Godia, Silvia Perez-Fernandez, Miquel Gomez, Aleksandra Mas-Stachurska, Helena Tizon-Marcos, Lluis Molina, Julio Marti-Almor, Merce Cladellas
Summary: This study aimed to detect atrial disease in CrS patients through analyzing atrial size and function. The results showed that patients with AF had larger atrial volume and worse atrial function, with an independent association between detection of AF and atrial volume, LAEF, and strain.
JOURNAL OF CLINICAL MEDICINE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Chia-Hung Yang, Hao-Tien Liu, Hui-Ling Lee, Fen-Chiung Lin, Chung-Chuan Chou
Summary: This study found that left atrial function plays an important role in the absence of atrial fibrillation (AF) in patients with left atrial dimension greater than or equal to 50 mm, and the late diastolic component of left atrial strain rate is the only independent variable associated with AF.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2022)
Review
Medicine, General & Internal
Riyaz A. Kaba, Aziz Momin, John Camm
Summary: Atrial fibrillation is a global disease associated with various serious complications, making treatment complex and challenging. A large proportion of patients have persistent or long-standing persistent AF, posing greater treatment challenges.
JOURNAL OF CLINICAL MEDICINE
(2021)
Article
Cardiac & Cardiovascular Systems
Chan Soon Park, Eue-Keun Choi, So-Ryoung Lee, Hyo-Jeong Ahn, Soonil Kwon, Sunhwa Kim, Suk Ho Sohn, Jae Woong Choi, Ho Young Hwang, Seil Oh
Summary: In patients with persistent AF and a large LA, there was no significant difference in prognosis between RFCA, CBA, and thoracoscopic maze procedures. Early recurrence during the blanking period predicted late recurrence in catheter ablation, but not in thoracoscopic maze surgery.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2022)
Article
Cardiac & Cardiovascular Systems
Cheryl Teres, David Soto-Iglesias, Diego Penela, Beatriz Jauregui, Augusto Ordonez, Alfredo Chauca, Jose Miguel Carreno, Claudia Scherer, Rodolfo San Antonio, Marina Huguet, Albert Roque, Carlos Ramirez, Guillermo Oller, Agusti Jornet, Jordi Palet, David Santana, Alejandro Panaro, Giuliana Maldonado, Gustavo de Leon, Gustavo Jimenez, Arturo Evangelista, Julio Carballo, Jose-Tomas Ortiz-Perez, Antonio Berruezo
Summary: The study aimed to determine the feasibility, effectiveness, and safety of adapting the ablation index (AI) to the left atrial wall thickness (LAWT) during paroxysmal atrial fibrillation (PAF) ablation. The results showed that personalized AF ablation based on LAWT allowed for pulmonary vein isolation with low RF delivery, fluoroscopy, and procedure time, with a high rate of first-pass isolation and a high freedom from AF recurrences in the patient population.
Article
Cardiac & Cardiovascular Systems
Yameng Shao, Lei Chen, Wensu Chen, Chuanyi Sang, Changjiang Xu, Chaoqun Zhang
Summary: Epicardial adipose tissue (EAT) is related to the presence of left atrial low voltage zones (LVZ) in patients with non-valvular atrial fibrillation (NVAF). EAT volume and attenuation values can independently predict the presence of LVZ.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2022)
Article
Multidisciplinary Sciences
Mary Luz Mojica-Pisciotti, Roman Panovsky, Lucia Masarova, Martin Pesl, Zdenek Starek, Tomas Holecek, Vera Feitova, Lukas Opatril, Katarina Dolezalova, Vladimir Kincl
Summary: This study assessed the left atrial phasic function and deformation in paroxysmal AF patients and found that they had larger left atrial volumes, lower left atrial emptying fraction, and lower left atrial longitudinal strain compared to controls.
SCIENTIFIC REPORTS
(2022)
Review
Cardiac & Cardiovascular Systems
Lavinia-Lucia Matei, Roxana-Mihaela Popescu, Andreea Catarina Popescu, Serban Mihai Balanescu
Summary: Atrial fibrillation (AF) is a result of structural and electrical remodeling of the atria, particularly the left atrium (LA), and LA changes are recognized as important prognostic markers. Echocardiography is a widely available and noninvasive method used to monitor the form and function of the LA. Early functional remodeling of the LA precedes anatomical alterations. Advanced echocardiographic techniques can evaluate impaired LA functions and reduced atrial compliance due to atrial fibrosis. Functional evaluation of the LA provides prognostic information about the risk of AF.
REVIEWS IN CARDIOVASCULAR MEDICINE
(2022)
Article
Biology
Jorge Duenas-Pamplona, Javier Garcia Garcia, Jose Sierra-Pallares, Conrado Ferrera, Rafael Agujetas, Jose Ramon Lopez-Minguez
Summary: This study aims to conduct an extensive study on the different assumptions commonly made in atrial simulations for AF patients, as well as to evaluate and compare the range of indices used to assess thrombus formation within the left atrium appendage (LAA). Results suggest the importance of validating the rigid atrium hypothesis and propose a new thrombosis predicting index, M4, which is shown to predict stasis more effectively than other indicators.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Review
Cardiac & Cardiovascular Systems
Adrian D. Elliott, Jonathan Ariyaratnam, Erin J. Howden, Andre La Gerche, Prashanthan Sanders
Summary: The left atrium (LA) plays a critical role in receiving pulmonary venous return and modulating left ventricular filling, with its function contributing to the augmentation in stroke volume during exercise. Structural remodeling and dysfunction of the LA are associated with adverse outcomes in cardiovascular disease, leading to exercise intolerance and increased risk of hospital admissions and mortality. Exercise training is recommended in patients with cardiovascular disease to improve outcomes and maintain functional capacity, with less attention given to the changes in LA structure and function compared to the left ventricle.
AMERICAN JOURNAL OF PHYSIOLOGY-HEART AND CIRCULATORY PHYSIOLOGY
(2023)
Article
Cardiac & Cardiovascular Systems
Xiangwei Ding, Mingfang Li, Hongwu Chen, Gang Yang, Fengxiang Zhang, Weizhu Ju, Kai Gu, Jianqing Li, Minglong Chen
Summary: This study found that the presence of low voltage area (LVA) at the anterior wall of the left atrium is associated with a history of thromboembolism (TE) in clinically low-risk patients with non-valvular atrial fibrillation (NVAF) and a low CHA(2)DS(2)-VASc score.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Ling Lin, Xu-Hui Zhou, Mei Zheng, Qiu-Xia Xie, Qian Tao, Hildo J. Lamb
Summary: This study evaluated the role of contour-based registration in quantification of extracellular volume fraction (ECV) and investigated normal segment-based myocardial ECV values at 3T. The results showed that contour-based registration improved the image quality of ECV maps and increased the precision of ECV quantification in cases demonstrating ventricular misregistration among source images.
Article
Cardiac & Cardiovascular Systems
Luuk H. G. A. Hopman, Julia E. Visch, Pranav Bhagirath, Anja M. van der Laan, Mark J. Mulder, Orod Razeghi, Michiel J. B. Kemme, Steven A. Niederer, Cornelis P. Allaart, Marco J. W. Gotte
Summary: This study found that right atrial remodeling parameters were not predictive of atrial fibrillation (AF) recurrence after AF ablation. Bi-atrial fibrotic remodeling is present in patients with AF, and the amount of left atrial (LA) fibrosis has a strong correlation with the amount of right atrial (RA) fibrosis.
EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING
(2023)
Article
Cardiac & Cardiovascular Systems
Uxio Hermida, Milou P. M. van Poppel, David F. A. Lloyd, Johannes K. Steinweg, Trisha V. Vigneswaran, John M. Simpson, Reza Razavi, Adelaide De Vecchi, Kuberan Pushparajah, Pablo Lamata
Summary: This study presents a statistical shape modeling pipeline to analyze the role and predictive value of arch shape in the antenatal diagnosis of CoA. By using fetal CMR data, a statistical shape model was built and achieved a high classification accuracy. This research provides novel insights for improving the antenatal diagnosis of CoA.
JOURNAL OF CARDIOVASCULAR TRANSLATIONAL RESEARCH
(2023)
Article
Cardiac & Cardiovascular Systems
Jeroen Venlet, Sebastiaan R. Piers, Jarieke Hoogendoorn, Alexander F. A. Androulakis, Marta de Riva, Rob J. van der Geest, Katja Zeppenfeld
Summary: Transmural activation delay in right ventricular cardiomyopathy can be used to identify ventricular tachycardia substrates, which is important for treatment.
Editorial Material
Cardiac & Cardiovascular Systems
Aurelien Bustin, Matthias Stuber, Maxime Sermesant, Hubert Cochet
EUROPEAN HEART JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Xianqiang Bao, Shuangyi Wang, Lingling Zheng, Richard James Housden, Joseph V. Hajnal, Kawal Rhode
Summary: This article proposes an ultrasound robot that integrates force control, force/torque measurement, and online adjustment mechanisms to address the concerns in medical ultrasound. The robot can measure, adjust, and eliminate operating forces, and achieve various scanning depths based on clinical requirements. Simulations and experiments show that the robot performs well in detecting forces and torques, maintaining constant operating force, and achieving different scanning depths.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Cardiac & Cardiovascular Systems
Harminder Gill, Joao Filipe Fernandes, Amanda Nio, Cameron Dockerill, Nili Shah, Naajia Ahmed, Jason Raymond, Shu Wang, Julio Sotelo, Jesus Urbina, Sergio Uribe, Ronak Rajani, Kawal Rhode, Pablo Lamata
Summary: This study focuses on imaging techniques and pressure drops in aortic stenosis. A customisable and cost-effective valve phantom circuit was developed to replicate valve mechanics and pathology. The reproducibility and relationship of different hemodynamic metrics were assessed, and it was found that peak and net pressure drops are reliable indicators of stenotic burden, while peak-to-peak pressure drop is confounded by non-valvular factors and should be reconsidered in clinical practice.
JOURNAL OF CARDIOVASCULAR TRANSLATIONAL RESEARCH
(2023)
Article
Robotics
Zicong Wu, Mikel De Iturrate Reyzabal, S. M. Hadi Sadati, Hongbin Liu, Sebastien Ourselin, Daniel Leff, Robert K. K. Katzschmann, Kawal Rhode, Christos Bergeles
Summary: Soft robots that grow through eversion/apical extension are capable of navigating fragile environments inside the human body. This letter presents a physics-based model of a miniature steerable eversion growing robot. The robot's growing, steering, stiffening, and interaction capabilities are demonstrated. The study investigates the interaction between a steerable catheter and a growing sheath, and the behavior of the growing robot under different pressures and external forces. The simulations conducted within the SOFA framework align with extensive experimentation using a physical robot setup, showing a mean absolute error of 10-20% between simulation and experimental results for curvature values.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Engineering, Biomedical
Xiaohui Zhang, Elsa-Marie Otoo, Yubo Fan, Chunjing Tao, Tianmiao Wang, Kawal Rhode
Summary: By using the CPD-based deformation registration algorithm and the proposed virtual view generation algorithm, we developed a deformable autostereoscopic 3D augmented reality (AR) navigation framework for laparoscopic surgery. The depth perception and user experience of the 3D AR navigation were evaluated compared with the 2D AR display using in-vitro porcine heart and offline clinical laparoscopic images. The results showed that autostereoscopic 3D AR provided a more consistent spatial perception and shorter measuring time than 2D AR, although the user experience was worse. Conclusion: Autostereoscopic 3D AR has the potential to improve surgical outcomes and shorten operating time, but image blur and distortion need to be addressed. The precise registration and fluent visualization requirements make autostereoscopic 3D AR navigation for soft tissue more challenging. Significance: This work lays the groundwork for further development of laparoscopic surgical navigation.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Article
Automation & Control Systems
Lukas Lindenroth, Danail Stoyanov, Kawal Rhode, Hongbin Liu
Summary: This study investigates the intrinsic sensing and subsequent control of contact forces in a hydraulic parallel soft robot. An algorithm for static, quasi-static, and dynamic force sensing is derived and validated for a single actuator. The results demonstrate accurate estimation of axial forces, and the force sensing methodology is applied to force control.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Automation & Control Systems
Xianqiang Bao, Shuangyi Wang, Lingling Zheng, Richard James Housden, Joseph Hajnal, Kawal Rhode
Summary: This article proposes a novel self-adaptive parallel manipulator (SAPM) for robotic ultrasonography. The SAPM can automatically adjust the ultrasound probe pose, provide approximate constant operating forces/torques, achieve mechanical measurement, and cushion undesired produced forces. Experimental results show that the SAPM can provide 3 DOFs motion, operating force/torque measurement, and automatically adjust the US probe pose to capture high-quality ultrasound images.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Yiping Xie, Jun Guo, Zhaokun Deng, Xilong Hou, James Housden, Kawal Rhode, Hongbin Liu, Zeng-Guang Hou, Shuangyi Wang
Summary: This article proposed a new compact mechanism design and a virtual admittance-based master-slave control method for the robotic transesophageal ultrasound system, improving its usability and conforming to the control habits of ultrasound scanning.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Computer Science, Information Systems
Carlos Martin-Isla, Victor M. Campello, Cristian Izquierdo, Kaisar Kushibar, Carla Sendra-Balcells, Polyxeni Gkontra, Alireza Sojoudi, Mitchell J. Fulton, Tewodros Weldebirhan Arega, Kumaradevan Punithakumar, Lei Li, Xiaowu Sun, Yasmina Al Khalil, Di Liu, Sana Jabbar, Sandro Queiros, Francesco Galati, Moona Mazher, Zheyao Gao, Marcel Beetz, Lennart Tautz, Christoforos Galazis, Marta Varela, Markus Huellebrand, Vicente Grau, Xiahai Zhuang, Domenec Puig, Maria A. Zuluaga, Hassan Mohy-ud-Din, Dimitris Metaxas, Marcel Breeuwer, Rob J. van der Geest, Michelle Noga, Stephanie Bricq, Mark E. Rentschler, Andrea Guala, Steffen E. Petersen, Sergio Escalera, Jose F. Rodriguez Palomares, Karim Lekadir
Summary: In recent years, deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies, but the segmentation of the right ventricle remains challenging. The M&Ms-2 challenge was organized to promote research on right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. The solutions provided by the participants show that nnU-Net achieved the best overall results, but multi-view approaches were able to capture additional information, highlighting the need for integrating multiple factors for reliable automatic cardiac segmentation algorithms.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Engineering, Biomedical
Benjamin Jackson, William Crinnion, Mikel De Iturrate Reyzabal, Harry Robertshaw, Christos Bergeles, Kawal Rhode, Thomas Booth
Summary: This study investigated the impact of different human-robot interfaces on endovascular surgical performance in interventional radiology simulations. The results showed that a device-mimicking controller was more suitable for controlling interventional neuroradiology procedures compared to joystick control approaches.
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
(2023)
Article
Critical Care Medicine
Phung Tran Huy Nhat, Nguyen Van Hao, Phan Vinh Tho, Hamideh Kerdegari, Luigi Pisani, Le Ngoc Minh Thu, Le Thanh Phuong, Ha Thi Hai Duong, Duong Bich Thuy, Angela McBride, Miguel Xochicale, Marcus Schultz, Reza Razavi, Andrew King, Louise Thwaites, Nguyen Van Vinh Chau, Sophie Yacoub, Alberto Gomez
Summary: This study developed an AI solution to assist clinicians in interpreting lung ultrasound images and evaluated its usefulness in a low resource ICU. The results showed that non-expert clinicians significantly improved their accuracy, time, and confidence in interpreting lung ultrasound images when using the AI tool.
Article
Computer Science, Artificial Intelligence
Hong Liu, Dong Wei, Donghuan Lu, Xiaoying Tang, Liansheng Wang, Yefeng Zheng
Summary: This study proposes a framework based on hybrid 2D-3D convolutional neural networks for obtaining continuous 3D retinal layer surfaces from OCT volumes. The framework works well with both full and sparse annotations and utilizes alignment displacement vectors and layer segmentation to align the B-scans and segment the layers. Experimental results show that the framework outperforms state-of-the-art 2D deep learning methods in terms of layer segmentation accuracy and cross-B-scan 3D continuity.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Simon Oxenford, Ana Sofia Rios, Barbara Hollunder, Clemens Neudorfer, Alexandre Boutet, Gavin J. B. Elias, Jurgen Germann, Aaron Loh, Wissam Deeb, Bryan Salvato, Leonardo Almeida, Kelly D. Foote, Robert Amaral, Paul B. Rosenberg, David F. Tang-Wai, David A. Wolk, Anna D. Burke, Marwan N. Sabbagh, Stephen Salloway, M. Mallar Chakravarty, Gwenn S. Smith, Constantine G. Lyketsos, Michael S. Okun, William S., Zoltan Mari, Francisco A. Ponce, Andres Lozano, Wolf-Julian Neumann, Bassam Al-Fatly, Andreas Horn
Summary: Spatial normalization is a method to map subject brain images to an average template brain, allowing comparison of brain imaging results. We introduce a novel tool called WarpDrive, which enables manual refinements of image alignment after automated registration. The tool improves accuracy of data representation and aids in understanding patient outcomes.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ricards Marcinkevics, Patricia Reis Wolfertstetter, Ugne Klimiene, Kieran Chin-Cheong, Alyssia Paschke, Julia Zerres, Markus Denzinger, David Niederberger, Sven Wellmann, Ece Ozkan, Christian Knorr, Julia E. Vogt
Summary: This study presents interpretable machine learning models for predicting the diagnosis, management, and severity of suspected appendicitis using ultrasound images. The proposed models utilize concept bottleneck models (CBM) that facilitate interpretation and intervention by clinicians, without compromising performance or requiring time-consuming image annotation.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Jian-Qing Zheng, Ziyang Wang, Baoru Huang, Ngee Han Lim, Bartlomiej W. Papiez
Summary: This article introduces a new method for medical image registration, which utilizes a separable motion backbone and a residual aligner module to better handle the discontinuous motion of multiple neighboring objects. The proposed method achieves excellent registration results on abdominal CT scans and lung CT scans.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangqiong Wu, Guanghua Tan, Hongxia Luo, Zhilun Chen, Bin Pu, Shengli Li, Kenli Li
Summary: This study develops a user-friendly framework for the automated diagnosis of thyroid nodules in ultrasound videos, simulating the diagnostic workflow of radiologists. By interpreting image characteristics and modeling temporal contextual information, the efficiency and generalizability of the diagnosis can be improved.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Riddhish Bhalodia, Shireen Elhabian, Jadie Adams, Wenzheng Tao, Ladislav Kavan, Ross Whitaker
Summary: This paper introduces DeepSSM, a deep learning-based framework for image-to-shape modeling. By learning the functional mapping from images to low-dimensional shape descriptors, DeepSSM can directly infer statistical representation of anatomy from 3D images. Compared to traditional methods, DeepSSM eliminates the need for heavy manual preprocessing and segmentation, and significantly improves computational time.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Florentin Liebmann, Marco von Atzigen, Dominik Stutz, Julian Wolf, Lukas Zingg, Daniel Suter, Nicola A. Cavalcanti, Laura Leoty, Hooman Esfandiari, Jess G. Snedeker, Martin R. Oswald, Marc Pollefeys, Mazda Farshad, Philipp Furnstahl
Summary: This study presents a marker-less approach for automatic registration and real-time navigation of lumbar spinal fusion surgery using a deep neural network, avoiding radiation exposure and surgical errors. The method was validated on an ex-vivo surgery and a public dataset.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Piyush Tiwary, Kinjawl Bhattacharyya, A. P. Prathosh
Summary: Domain shift refers to the change of distributional characteristics between training and testing datasets, leading to performance drop. For medical image tasks, domain shift can be caused by changes in imaging modalities, devices, and staining mechanisms. Existing approaches based on generative models suffer from training difficulties and lack of diversity. In this paper, the authors propose the use of energy-based models (EBMs) for unpaired image-to-image translation in medical images. The proposed method, called Cycle Consistent Twin EBMs (CCT-EBM), employs a pair of EBMs in the latent space of an Auto-Encoder to ensure translation symmetry and coupling between domains.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Yutong Xie, Jianpeng Zhang, Lingqiao Liu, Hu Wang, Yiwen Ye, Johan Verjans, Yong Xia
Summary: This paper proposes a hybrid pre-training paradigm that combines self-supervised learning and supervised learning to improve the representation quality for medical image segmentation tasks. It introduces a reference task in self-supervised learning and optimizes the model using a gradient matching method. The experimental results demonstrate the effectiveness of this approach on multiple medical image segmentation benchmarks.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Youyi Song, Jing Zou, Kup-Sze Choi, Baiying Lei, Jing Qin
Summary: Cell classification is crucial for intelligent cervical cancer screening, but the variation in cells' appearance and shape poses challenges. A new learning algorithm, worse-case boosting, is proposed to improve classification accuracy for under-represented data. Experimental results demonstrate the effectiveness of this algorithm in two publicly available datasets, achieving a 4% improvement in accuracy.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Sangjoon Park, Eun Sun Lee, Kyung Sook Shin, Jeong Eun Lee, Jong Chul Ye
Summary: The increasing demand for AI systems to monitor human errors and abnormalities in healthcare presents challenges. This study presents a model called Medical X-VL, which is tailored for the medical domain and outperformed current state-of-the-art models in two medical image datasets. The model enables various zero-shot tasks for monitoring AI in the medical domain.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Anna Klimovskaia Susmelj, Berkan Lafci, Firat Ozdemir, Neda Davoudi, Xose Luis Dean-Ben, Fernando Perez-Cruz, Daniel Razansky
Summary: Optoacoustic imaging is a technique that uses optical excitation and ultrasound detection for biological tissue imaging. The quality of the images depends on the extent of tomographic coverage provided by the ultrasound detector arrays. However, full coverage is not always possible due to experimental constraints. The proposed signal domain adaptation network aims to reduce limited-view artifacts in the images.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Srijay Deshpande, Muhammad Dawood, Fayyaz Minhas, Nasir Rajpoot
Summary: In this work, a novel framework called SynCLay is proposed for automated synthesis of histology images based on user-defined cellular layouts. The framework can generate realistic and high-quality histology images with different cellular arrangements, which is helpful for studying the role of cells in the tumor microenvironment. The framework integrates a nuclear segmentation and classification model to refine nuclear structures and generate nuclear masks. Evaluation using quantitative metrics and feedback from pathologists shows that the synthetic images generated by SynCLay have high realism scores and can accurately differentiate between benign and malignant tumors.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber
Summary: Survival analysis is a valuable tool in healthcare for predicting the time to specific events. This paper introduces CenTime, a novel approach that directly estimates the time to event. The method performs well with censored data and can be easily integrated with deep learning models. Compared to standard methods, CenTime offers superior performance in predicting event time while maintaining comparable ranking performance.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Bingyuan Liu, Jose Dolz, Adrian Galdran, Riadh Kobbi, Ismail Ben Ayed
Summary: Most segmentation losses, such as CE and Dice, are variants of the Cross-Entropy or Dice losses. This work provides a theoretical analysis that shows a deeper connection between CE and Dice than previously thought. From a constrained-optimization perspective, both CE and Dice decompose into similar ground-truth matching terms and region-size penalty terms. The analysis uncovers hidden region-size biases: Dice has an intrinsic bias towards extremely imbalanced solutions, while CE implicitly encourages the ground-truth region proportions. Based on this analysis, a principled and simple solution is proposed to explicitly control the region-size bias.
MEDICAL IMAGE ANALYSIS
(2024)