-
Spatiotemporal knowledge teacher-student reinforcement learning to detect liver tumors without contrast agents Med. Image Anal. (IF 10.9) Pub Date : 2023-09-26 Chenchu Xu, Yuhong Song, Dong Zhang, Leonardo Kayat Bittencourt, Sree Harsha Tirumani, Shuo Li
-
A deep weakly semi-supervised framework for endoscopic lesion segmentation Med. Image Anal. (IF 10.9) Pub Date : 2023-09-20 Yuxuan Shi, Hong Wang, Haoqin Ji, Haozhe Liu, Yuexiang Li, Nanjun He, Dong Wei, Yawen Huang, Qi Dai, Jianrong Wu, Xinrong Chen, Yefeng Zheng, Hongmeng Yu
-
Memory-based unsupervised video clinical quality assessment with multi-modality data in fetal ultrasound Med. Image Anal. (IF 10.9) Pub Date : 2023-09-23 He Zhao, Qingqing Zheng, Clare Teng, Robail Yasrab, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble
In obstetric sonography, the quality of acquisition of ultrasound scan video is crucial for accurate (manual or automated) biometric measurement and fetal health assessment. However, the nature of fetal ultrasound involves free-hand probe manipulation and this can make it challenging to capture high-quality videos for fetal biometry, especially for the less-experienced sonographer. Manually checking
-
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images Med. Image Anal. (IF 10.9) Pub Date : 2023-09-23 Marcel Beetz, Abhirup Banerjee, Julius Ossenberg-Engels, Vicente Grau
Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and physiology. In this paper, we propose a novel
-
Learning joint surface reconstruction and segmentation, from brain images to cortical surface parcellation Med. Image Anal. (IF 10.9) Pub Date : 2023-09-22 Karthik Gopinath, Christian Desrosiers, Herve Lombaert
-
Backdoor attack and defense in federated generative adversarial network-based medical image synthesis Med. Image Anal. (IF 10.9) Pub Date : 2023-09-22 Ruinan Jin, Xiaoxiao Li
-
GAMMA challenge: Glaucoma grAding from Multi-Modality imAges Med. Image Anal. (IF 10.9) Pub Date : 2023-09-18 Junde Wu, Huihui Fang, Fei Li, Huazhu Fu, Fengbin Lin, Jiongcheng Li, Yue Huang, Qinji Yu, Sifan Song, Xinxing Xu, Yanyu Xu, Wensai Wang, Lingxiao Wang, Shuai Lu, Huiqi Li, Shihua Huang, Zhichao Lu, Chubin Ou, Xifei Wei, Bingyuan Liu, Yanwu Xu
Glaucoma is a chronic neuro-degenerative condition that is one of the world’s leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective
-
Latent Transformer Models for out-of-distribution detection Med. Image Anal. (IF 10.9) Pub Date : 2023-09-16 Mark S. Graham, Petru-Daniel Tudosiu, Paul Wright, Walter Hugo Lopez Pinaya, Petteri Teikari, Ashay Patel, Jean-Marie U-King-Im, Yee H. Mah, James T. Teo, Hans Rolf Jäger, David Werring, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
Any clinically-deployed image-processing pipeline must be robust to the full range of inputs it may be presented with. One popular approach to this challenge is to develop predictive models that can provide a measure of their uncertainty. Another approach is to use generative modelling to quantify the likelihood of inputs. Inputs with a low enough likelihood are deemed to be out-of-distribution and
-
Spatio-temporal physics-informed learning: A novel approach to CT perfusion analysis in acute ischemic stroke Med. Image Anal. (IF 10.9) Pub Date : 2023-09-15 Lucas de Vries, Rudolf L.M. van Herten, Jan W. Hoving, Ivana Išgum, Bart J. Emmer, Charles B.L.M. Majoie, Henk A. Marquering, Efstratios Gavves
-
Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography Med. Image Anal. (IF 10.9) Pub Date : 2023-09-15 Yi Gu, Yoshito Otake, Keisuke Uemura, Mazen Soufi, Masaki Takao, Hugues Talbot, Seiji Okada, Nobuhiko Sugano, Yoshinobu Sato
-
Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge Med. Image Anal. (IF 10.9) Pub Date : 2023-09-18 Vincent Andrearczyk, Valentin Oreiller, Sarah Boughdad, Catherine Cheze Le Rest, Olena Tankyevych, Hesham Elhalawani, Mario Jreige, John O. Prior, Martin Vallières, Dimitris Visvikis, Mathieu Hatt, Adrien Depeursinge
-
Dynamic feature splicing for few-shot rare disease diagnosis Med. Image Anal. (IF 10.9) Pub Date : 2023-09-16 Yuanyuan Chen, Xiaoqing Guo, Yongsheng Pan, Yong Xia, Yixuan Yuan
Annotated images for rare disease diagnosis are extremely hard to collect. Therefore, identifying rare diseases under a few-shot learning (FSL) setting is significant. Existing FSL methods transfer useful and global knowledge from base classes with abundant training samples to enrich features of novel classes with few training samples, but still face difficulties when being applied to medical images
-
One-shot segmentation of novel white matter tracts via extensive data augmentation and adaptive knowledge transfer Med. Image Anal. (IF 10.9) Pub Date : 2023-09-15 Wan Liu, Zhizheng Zhuo, Yaou Liu, Chuyang Ye
The use of convolutional neural networks (CNNs) has allowed accurate white matter (WM) tract segmentation on diffusion magnetic resonance imaging (dMRI). To train the CNN-based segmentation models, a large number of scans on which WM tracts are annotated need to be collected, and these annotated scans can be accumulated over a long period of time. However, when novel WM tracts that are different from
-
Joint localization and classification of breast masses on ultrasound images using an auxiliary attention-based framework Med. Image Anal. (IF 10.9) Pub Date : 2023-09-14 Zong Fan, Ping Gong, Shanshan Tang, Christine U. Lee, Xiaohui Zhang, Pengfei Song, Shigao Chen, Hua Li
-
A robust and interpretable deep learning framework for multi-modal registration via keypoints Med. Image Anal. (IF 10.9) Pub Date : 2023-09-13 Alan Q. Wang, Evan M. Yu, Adrian V. Dalca, Mert R. Sabuncu
We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for registration often are not robust to large misalignments, are not interpretable, and do not incorporate the symmetries of the problem. In addition, most models produce only a single prediction at test-time. Our core insight
-
Tumor radiogenomics in gliomas with Bayesian layered variable selection Med. Image Anal. (IF 10.9) Pub Date : 2023-09-12 Shariq Mohammed, Sebastian Kurtek, Karthik Bharath, Arvind Rao, Veerabhadran Baladandayuthapani
-
Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation Med. Image Anal. (IF 10.9) Pub Date : 2023-09-12 Hyojoon Park, Bin Li, Yuming Liu, Michael S. Nelson, Helen M. Wilson, Eftychios Sifakis, Kevin W. Eliceiri
-
The role of noise in denoising models for anomaly detection in medical images Med. Image Anal. (IF 10.9) Pub Date : 2023-09-11 Antanas Kascenas, Pedro Sanchez, Patrick Schrempf, Chaoyang Wang, William Clackett, Shadia S. Mikhael, Jeremy P. Voisey, Keith Goatman, Alexander Weir, Nicolas Pugeault, Sotirios A. Tsaftaris, Alison Q. O’Neil
Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance
-
Deep learning, data ramping, and uncertainty estimation for detecting artifacts in large, imbalanced databases of MRI images Med. Image Anal. (IF 10.9) Pub Date : 2023-09-09 Ricardo Pizarro, Haz-Edine Assemlal, Sethu K. Boopathy Jegathambal, Thomas Jubault, Samson Antel, Douglas Arnold, Amir Shmuel
Magnetic resonance imaging (MRI) is increasingly being used to delineate morphological changes underlying neurological disorders. Successfully detecting these changes depends on the MRI data quality. Unfortunately, image artifacts frequently compromise the MRI utility, making it critical to screen the data. Currently, quality assessment requires visual inspection, a time-consuming process that suffers
-
A generic fundus image enhancement network boosted by frequency self-supervised representation learning Med. Image Anal. (IF 10.9) Pub Date : 2023-09-09 Heng Li, Haofeng Liu, Huazhu Fu, Yanwu Xu, Hai Shu, Ke Niu, Yan Hu, Jiang Liu
-
Multi-site, Multi-domain Airway Tree Modeling Med. Image Anal. (IF 10.9) Pub Date : 2023-09-09 Minghui Zhang, Yangqian Wu, Hanxiao Zhang, Yulei Qin, Hao Zheng, Wen Tang, Corey Arnold, Chenhao Pei, Pengxin Yu, Yang Nan, Guang Yang, Simon Walsh, Dominic C. Marshall, Matthieu Komorowski, Puyang Wang, Dazhou Guo, Dakai Jin, Ya’nan Wu, Shuiqing Zhao, Runsheng Chang, Yun Gu
Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT’09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven
-
Active learning for medical image segmentation with stochastic batches Med. Image Anal. (IF 10.9) Pub Date : 2023-09-12 Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data is particularly difficult for medical image segmentation tasks because of the limited expert availability and intensive manual effort required. To reduce manual labelling, active learning (AL) targets the most informative samples from the unlabelled set to annotate
-
Colonoscopy 3D video dataset with paired depth from 2D-3D registration Med. Image Anal. (IF 10.9) Pub Date : 2023-09-07 Taylor L. Bobrow, Mayank Golhar, Rohan Vijayan, Venkata S. Akshintala, Juan R. Garcia, Nicholas J. Durr
-
A clinically applicable AI system for diagnosis of congenital heart diseases based on computed tomography images Med. Image Anal. (IF 10.9) Pub Date : 2023-09-06 Xiaowei Xu, Qianjun Jia, Haiyun Yuan, Hailong Qiu, Yuhao Dong, Wen Xie, Zeyang Yao, Jiawei Zhang, Zhiqaing Nie, Xiaomeng Li, Yiyu Shi, James Y. Zou, Meiping Huang, Jian Zhuang
Congenital heart disease (CHD) is the most common type of birth defect. Without timely detection and treatment, approximately one-third of children with CHD would die in the infant period. However, due to the complicated heart structures, early diagnosis of CHD and its types is quite challenging, even for experienced radiologists. Here, we present an artificial intelligence (AI) system that achieves
-
YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation Med. Image Anal. (IF 10.9) Pub Date : 2023-08-30 Li Lin, Linkai Peng, Huaqing He, Pujin Cheng, Jiewei Wu, Kenneth K.Y. Wong, Xiaoying Tang
-
Ambiguity-aware breast tumor cellularity estimation via self-ensemble label distribution learning Med. Image Anal. (IF 10.9) Pub Date : 2023-09-03 Xiangyu Li, Xinjie Liang, Gongning Luo, Wei Wang, Kuanquan Wang, Shuo Li
-
Deep learning with explainability for characterizing age-related intrinsic differences in dynamic brain functional connectivity Med. Image Anal. (IF 10.9) Pub Date : 2023-09-01 Chen Qiao, Bin Gao, Yuechen Liu, Xinyu Hu, Wenxing Hu, Vince D. Calhoun, Yu-Ping Wang
-
The value of Augmented Reality in surgery — A usability study on laparoscopic liver surgery Med. Image Anal. (IF 10.9) Pub Date : 2023-09-01 João Ramalhinho, Soojeong Yoo, Thomas Dowrick, Bongjin Koo, Murali Somasundaram, Kurinchi Gurusamy, David J. Hawkes, Brian Davidson, Ann Blandford, Matthew J. Clarkson
-
Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration Med. Image Anal. (IF 10.9) Pub Date : 2023-08-26 Yiwen Li, Yunguan Fu, Iani J.M.B. Gayo, Qianye Yang, Zhe Min, Shaheer U. Saeed, Wen Yan, Yipei Wang, J. Alison Noble, Mark Emberton, Matthew J. Clarkson, Henkjan Huisman, Dean C. Barratt, Victor A. Prisacariu, Yipeng Hu
The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations. This work describes a fully 3D prototypical few-shot segmentation algorithm, such that the trained networks can be effectively adapted
-
UNesT: Local spatial representation learning with hierarchical transformer for efficient medical segmentation Med. Image Anal. (IF 10.9) Pub Date : 2023-08-25 Xin Yu, Qi Yang, Yinchi Zhou, Leon Y. Cai, Riqiang Gao, Ho Hin Lee, Thomas Li, Shunxing Bao, Zhoubing Xu, Thomas A. Lasko, Richard G. Abramson, Zizhao Zhang, Yuankai Huo, Bennett A. Landman, Yucheng Tang
-
Multi-cell type and multi-level graph aggregation network for cancer grading in pathology images Med. Image Anal. (IF 10.9) Pub Date : 2023-08-25 Syed Farhan Abbas, Trinh Thi Le Vuong, Kyungeun Kim, Boram Song, Jin Tae Kwak
In pathology, cancer grading is crucial for patient management and treatment. Recent deep learning methods, based upon convolutional neural networks (CNNs), have shown great potential for automated and accurate cancer diagnosis. However, these do not explicitly utilize tissue/cellular composition, and thus difficult to incorporate the existing knowledge of cancer pathology. In this study, we propose
-
A-GCL: Adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders Med. Image Anal. (IF 10.9) Pub Date : 2023-08-22 Shengjie Zhang, Xiang Chen, Xin Shen, Bohan Ren, Ziqi Yu, Haibo Yang, Xi Jiang, Dinggang Shen, Yuan Zhou, Xiao-Yong Zhang
Accurate diagnosis of neurodevelopmental disorders is a challenging task due to the time-consuming cognitive tests and potential human bias in clinics. To address this challenge, we propose a novel adversarial self-supervised graph neural network (GNN) based on graph contrastive learning, named A-GCL, for diagnosing neurodevelopmental disorders using functional magnetic resonance imaging (fMRI) data
-
Multi-level and joint attention networks on brain functional connectivity for cross-cognitive prediction Med. Image Anal. (IF 10.9) Pub Date : 2023-08-21 Jing Xia, Nanguang Chen, Anqi Qiu
Deep learning on resting-state functional MRI (rs-fMRI) has shown great success in predicting a single cognition or mental disease. Nevertheless, cognitive functions or mental diseases may share neural mechanisms that can benefit their prediction/classification. We propose a multi-level and joint attention (ML-Joint-Att) network to learn high-order representations of brain functional connectivities
-
Deformation equivariant cross-modality image synthesis with paired non-aligned training data Med. Image Anal. (IF 10.9) Pub Date : 2023-08-20 Joel Honkamaa, Umair Khan, Sonja Koivukoski, Mira Valkonen, Leena Latonen, Pekka Ruusuvuori, Pekka Marttinen
Cross-modality image synthesis is an active research topic with multiple medical clinically relevant applications. Recently, methods allowing training with paired but misaligned data have started to emerge. However, no robust and well-performing methods applicable to a wide range of real world data sets exist. In this work, we propose a generic solution to the problem of cross-modality image synthesis
-
Nested star-shaped objects segmentation using diameter annotations Med. Image Anal. (IF 10.9) Pub Date : 2023-08-19 Robin Camarasa, Hoel Kervadec, M. Eline Kooi, Jeroen Hendrikse, Paul J. Nederkoorn, Daniel Bos, Marleen de Bruijne
Most current deep learning based approaches for image segmentation require annotations of large datasets, which limits their application in clinical practice. We observe a mismatch between the voxelwise ground-truth that is required to optimize an objective at a voxel level and the commonly used, less time-consuming clinical annotations seeking to characterize the most important information about the
-
Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images Med. Image Anal. (IF 10.9) Pub Date : 2023-08-18 Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh, Gustavo Carneiro
Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy) images only, but during testing, they are able to classify normal and abnormal (or disease) images. UAD is an important medical image analysis (MIA) method to be applied in disease screening problems because the training sets available for those problems usually contain only normal images. However, the exclusive reliance
-
DefCor-Net: Physics-aware ultrasound deformation correction Med. Image Anal. (IF 10.9) Pub Date : 2023-08-18 Zhongliang Jiang, Yue Zhou, Dongliang Cao, Nassir Navab
The recovery of morphologically accurate anatomical images from deformed ones is challenging in ultrasound (US) image acquisition, but crucial to accurate and consistent diagnosis, particularly in the emerging field of computer-assisted diagnosis. This article presents a novel physics-aware deformation correction approach based on a coarse-to-fine, multi-scale deep neural network (DefCor-Net). To achieve
-
From WSI-level to patch-level: Structure prior-guided binuclear cell fine-grained detection Med. Image Anal. (IF 10.9) Pub Date : 2023-08-12 Geng Hu, Baomin Wang, Boxian Hu, Dan Chen, Lihua Hu, Cheng Li, Yu An, Guiping Hu, Guang Jia
Accurate and quick binuclear cell (BC) detection plays a significant role in predicting the risk of leukemia and other malignant tumors. However, manual counting of BCs using microscope images is time consuming and subjective. Moreover, traditional image processing approaches perform poorly due to the limitations in staining quality and the diversity of morphological features in binuclear cell (BC)
-
Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer’s disease – A systematic model evaluation Med. Image Anal. (IF 10.9) Pub Date : 2023-08-14 A. Nemali, N. Vockert, D. Berron, A. Maas, J. Bernal, R. Yakupov, O. Peters, D. Gref, N. Cosma, L. Preis, J. Priller, E. Spruth, S. Altenstein, A. Lohse, K. Fliessbach, O. Kimmich, I. Vogt, J. Wiltfang, N. Hansen, C. Bartels, G. Ziegler
Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer’s disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly
-
Nuclei segmentation with point annotations from pathology images via self-supervised learning and co-training Med. Image Anal. (IF 10.9) Pub Date : 2023-08-14 Yi Lin, Zhiyong Qu, Hao Chen, Zhongke Gao, Yuexiang Li, Lili Xia, Kai Ma, Yefeng Zheng, Kwang-Ting Cheng
Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point
-
Monocular endoscope 6-DoF tracking with constrained evolutionary stochastic filtering Med. Image Anal. (IF 10.9) Pub Date : 2023-08-11 Xiongbiao Luo, Lixin Xie, Hui-Qing Zeng, Xiaoying Wang, Shiyue Li
-
USE-Evaluator: Performance metrics for medical image segmentation models supervised by uncertain, small or empty reference annotations in neuroimaging Med. Image Anal. (IF 10.9) Pub Date : 2023-08-10 Sophie Ostmeier, Brian Axelrod, Fabian Isensee, Jeroen Bertels, Michael Mlynash, Soren Christensen, Maarten G. Lansberg, Gregory W. Albers, Rajen Sheth, Benjamin F.J. Verhaaren, Abdelkader Mahammedi, Li-Jia Li, Greg Zaharchuk, Jeremy J. Heit
Performance metrics for medical image segmentation models are used to measure the agreement between the reference annotation and the predicted segmentation. Usually, overlap metrics, such as the Dice, are used as a metric to evaluate the performance of these models in order for results to be comparable. However, there is a mismatch between the distributions of cases and the difficulty level of segmentation
-
Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis Med. Image Anal. (IF 10.9) Pub Date : 2023-08-09 Rafic Nader, Romain Bourcier, Florent Autrusseau
-
Stimulus-guided adaptive transformer network for retinal blood vessel segmentation in fundus images Med. Image Anal. (IF 10.9) Pub Date : 2023-08-09 Ji Lin, Xingru Huang, Huiyu Zhou, Yaqi Wang, Qianni Zhang
-
WarpPINN: Cine-MR image registration with physics-informed neural networks Med. Image Anal. (IF 10.9) Pub Date : 2023-08-09 Pablo Arratia López, Hernán Mella, Sergio Uribe, Daniel E. Hurtado, Francisco Sahli Costabal
The diagnosis of heart failure usually includes a global functional assessment, such as ejection fraction measured by magnetic resonance imaging. However, these metrics have low discriminate power to distinguish different cardiomyopathies, which may not affect the global function of the heart. Quantifying local deformations in the form of cardiac strain can provide helpful information, but it remains
-
MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data Med. Image Anal. (IF 10.9) Pub Date : 2023-08-09 Mahbaneh Eshaghzadeh Torbati, Davneet S. Minhas, Charles M. Laymon, Pauline Maillard, James D. Wilson, Chang-Le Chen, Ciprian M. Crainiceanu, Charles S. DeCarli, Seong Jae Hwang, Dana L. Tudorascu
Large-scale data obtained from aggregation of already collected multi-site neuroimaging datasets has brought benefits such as higher statistical power, reliability, and robustness to the studies. Despite these promises from growth in sample size, substantial technical variability stemming from differences in scanner specifications exists in the aggregated data and could inadvertently bias any downstream
-
Weakly supervised joint whole-slide segmentation and classification in prostate cancer Med. Image Anal. (IF 10.9) Pub Date : 2023-08-09 Pushpak Pati, Guillaume Jaume, Zeineb Ayadi, Kevin Thandiackal, Behzad Bozorgtabar, Maria Gabrani, Orcun Goksel
-
Generative appearance replay for continual unsupervised domain adaptation Med. Image Anal. (IF 10.9) Pub Date : 2023-08-07 Boqi Chen, Kevin Thandiackal, Pushpak Pati, Orcun Goksel
Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention
-
Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey Med. Image Anal. (IF 10.9) Pub Date : 2023-08-06 Anusha Aswath, Ahmad Alsahaf, Ben N.G. Giepmans, George Azzopardi
Electron microscopy (EM) enables high-resolution imaging of tissues and cells based on 2D and 3D imaging techniques. Due to the laborious and time-consuming nature of manual segmentation of large-scale EM datasets, automated segmentation approaches are crucial. This review focuses on the progress of deep learning-based segmentation techniques in large-scale cellular EM throughout the last six years
-
POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation Med. Image Anal. (IF 10.9) Pub Date : 2023-08-05 Xingru Huang, Retesh Bajaj, Yilong Li, Xin Ye, Ji Lin, Francesca Pugliese, Anantharaman Ramasamy, Yue Gu, Yaqi Wang, Ryo Torii, Jouke Dijkstra, Huiyu Zhou, Christos V. Bourantas, Qianni Zhang
-
Segment anything model for medical image analysis: An experimental study Med. Image Anal. (IF 10.9) Pub Date : 2023-08-02 Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu, Jichen Yang, Nicholas Konz, Yixin Zhang
-
R2Net: Efficient and flexible diffeomorphic image registration using Lipschitz continuous residual networks Med. Image Anal. (IF 10.9) Pub Date : 2023-08-01 Ankita Joshi, Yi Hong
Classical diffeomorphic image registration methods, while being accurate, face the challenges of high computational costs. Deep learning based approaches provide a fast alternative to address these issues; however, most existing deep solutions either lose the good property of diffeomorphism or have limited flexibility to capture large deformations, under the assumption that deformations are driven
-
Modeling and hexahedral meshing of cerebral arterial networks from centerlines Med. Image Anal. (IF 10.9) Pub Date : 2023-07-29 Méghane Decroocq, Carole Frindel, Pierre Rougé, Makoto Ohta, Guillaume Lavoué
-
Curriculum label distribution learning for imbalanced medical image segmentation Med. Image Anal. (IF 10.9) Pub Date : 2023-07-29 Xiangyu Li, Gongning Luo, Wei Wang, Kuanquan Wang, Shuo Li
-
Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise Med. Image Anal. (IF 10.9) Pub Date : 2023-07-28 Hendrik A. Mehrtens, Alexander Kurz, Tabea-Clara Bucher, Titus J. Brinker
In the past years, deep learning has seen an increase in usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-risk environments deep learning models need to be able to judge their uncertainty and be able to reject inputs when there is a significant chance of misclassification. In this work, we conduct a rigorous evaluation of the
-
CReg-KD: Model refinement via confidence regularized knowledge distillation for brain imaging Med. Image Anal. (IF 10.9) Pub Date : 2023-07-26 Yanwu Yang, Xutao Guo, Chenfei Ye, Yang Xiang, Ting Ma
One of the core challenges of deep learning in medical image analysis is data insufficiency, especially for 3D brain imaging, which may lead to model over-fitting and poor generalization. Regularization strategies such as knowledge distillation are powerful tools to mitigate the issue by penalizing predictive distributions and introducing additional knowledge to reinforce the training process. In this
-
CDDSA: Contrastive domain disentanglement and style augmentation for generalizable medical image segmentation Med. Image Anal. (IF 10.9) Pub Date : 2023-07-18 Ran Gu, Guotai Wang, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Yinan Chen, Wenjun Liao, Shichuan Zhang, Kang Li, Dimitris N. Metaxas, Shaoting Zhang
-
Robotic ultrasound imaging: State-of-the-art and future perspectives Med. Image Anal. (IF 10.9) Pub Date : 2023-07-18 Zhongliang Jiang, Septimiu E. Salcudean, Nassir Navab
Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. Robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy
-
Attractive deep morphology-aware active contour network for vertebral body contour extraction with extensions to heterogeneous and semi-supervised scenarios Med. Image Anal. (IF 10.9) Pub Date : 2023-07-18 Shen Zhao, Jinhong Wang, Xinxin Wang, Yikang Wang, Hanying Zheng, Bin Chen, An Zeng, Fuxin Wei, Sadeer Al-Kindi, Shuo Li
-
Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse Med. Image Anal. (IF 10.9) Pub Date : 2023-07-17 Simona Bottani, Ninon Burgos, Aurélien Maire, Dario Saracino, Sebastian Ströer, Didier Dormont, Olivier Colliot,