The success of deep learning methods relies on the availability of a large number of datasets with annotations; however, curating such datasets is burdensome, especially for medical images. To relieve such a burden for a landmark detection task, we explore the feasibility of using only a single annotated image and propose a novel framework named Cascade Comparing to Detect (CC2D) for one-shot landmark detection. CC2D consists of two stages: 1) Self-supervised learning (CC2D-SSL) and 2) Training with pseudo-labels (CC2D-TPL). CC2D-SSL captures the consistent anatomical information in a coarse-to-fine fashion by comparing the cascade feature representations and generates predictions on the training set. CC2D-TPL further improves the performance by training a new landmark detector with those predictions. The effectiveness of CC2D is evaluated on a widely-used public dataset of cephalometric landmark detection, which achieves a competitive detection accuracy of 81.01\% within 4.0mm, comparable to the state-of-the-art fully-supervised methods using a lot more than one training image.
Landmarks Are Alike Yet Distinct: Harnessing Similarity and Individuality for One-Shot Medical Landmark Detection
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；School of Information Science and Technology, Eastern Institute of Technology (EIT)（东方理工大学信息科学与技术学院）；Stanford University（斯坦福大学）
Centrum Wiskunde & Informatica（数学与计算机科学国家研究所）；Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹大学医学中心）；Delft University of Technology（代尔夫特理工大学）
Anatomical landmark correspondences in medical images can provide additional guidance information for the alignment of two images, which, in turn, is crucial for many medical applications. However, manual landmark annotation is labor-intensive. Therefore, we propose an end-to-end deep learning approach to automatically detect landmark correspondences in pairs of two-dimensional (2D) images. Our approach consists of a Siamese neural network, which is trained to identify salient locations in images as landmarks and predict matching probabilities for landmark pairs from two different images. We t…
Centrum Wiskunde & Informatica（荷兰国家数学与计算机科学研究所）；Amsterdam University Medical Centers（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；Delft University of Technology（代尔夫特理工大学）；Leiden University Medical Center（莱顿大学医学中心）
Purpose: Deformable Image Registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudied, especially due to the lack of automatic landmark detection methods for three-dimensional (3D) medical images. Approach: We present a Deep Convolutional Neural Network (DCNN), called DCNN-Match, that learns to predict landmark correspondences in 3D images in a self-supervised manner. We trained DCNN-Match on pairs of Computed Tomography (CT) scans containing simulated deformations. We explored five variants of DCNN…
H3DE-Net: Efficient and Accurate 3D Landmark Detection in Medical Imaging
School of Computer Science USTC（计算机科学学院 中国科学技术大学）；School of Computer Science（计算机科学学院）；Shanghai Jiao Tong University（上海交通大学）；ShanghaiTech University（上海科技大学）；Sciences College（科学学院）；School of Information Science, NENU（信息科学学院 南京师范大学）；Technical University of Munich (TUM)（慕尼黑技术大学）；Stanford University（斯坦福大学）
University Medical Center Utrecht（乌得勒支大学医学中心）；Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹医学中心）；University of Twente（特文特大学）；Wageningen University（瓦赫宁根大学）
In this study, we propose a fast and accurate method to automatically localize anatomical landmarks in medical images. We employ a global-to-local localization approach using fully convolutional neural networks (FCNNs). First, a global FCNN localizes multiple landmarks through the analysis of image patches, performing regression and classification simultaneously. In regression, displacement vectors pointing from the center of image patches towards landmark locations are determined. In classification, presence of landmarks of interest in the patch is established. Global landmark locations are o…
UAE: Universal Anatomical Embedding on Multi-modality Medical Images
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University（西北工业大学计算机科学与工程学院，空天地海一体化大数据应用技术国家工程实验室）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；Beijing United Family Hospital（北京和睦家医院）；The First Affiliated Hospital, Zhejiang University（浙江大学医学院附属第一医院）；Peking Union Medical College Hospital（北京协和医院）
Shanghai Jiao Tong University（上海交通大学）；University of Sydney（悉尼大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院瑞金医院）
Motion estimation is a fundamental step in dynamic medical image processing for the assessment of target organ anatomy and function. However, existing image-based motion estimation methods, which optimize the motion field by evaluating the local image similarity, are prone to produce implausible estimation, especially in the presence of large motion. In this study, we provide a novel motion estimation framework of Dense-Sparse-Dense (DSD), which comprises two stages. In the first stage, we process the raw dense image to extract sparse landmarks to represent the target organ anatomical topology…
Image Sciences Institute（影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Department of Radiology（放射科）
Fast and accurate anatomical landmark detection can benefit many medical image analysis methods. Here, we propose a method to automatically detect anatomical landmarks in medical images. Automatic landmark detection is performed with a patch-based fully convolutional neural network (FCNN) that combines regression and classification. For any given image patch, regression is used to predict the 3D displacement vector from the image patch to the landmark. Simultaneously, classification is used to identify patches that contain the landmark. Under the assumption that patches close to a landmark can…
Adaptive Query Prompting for Multi-Domain Landmark Detection
School of Computer Science and Technology, Guangdong University of Technology（广东技术大学计算机科学与技术学院）；School of Automation, Guangdong University of Technology（广东技术大学自动化学院）；Faculty of Applied Sciences, Macao Polytechnic University（澳门 polytechnic university 应用科学学院）；School of Information Science and Technology, Guangdong University of Foreign Studies（广东外语外贸大学信息科学与技术学院）；School of Computer Science and Engineering, Huizhou University（惠州市大学计算机科学与工程学院）
The University of Hong Kong（香港大学）；ShanghaiTech University（上海科技大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；South China University of Technology（华南理工大学）
Well-annotated medical images are costly and sometimes even impossible to acquire, hindering landmark detection accuracy to some extent. Semi-supervised learning alleviates the reliance on large-scale annotated data by exploiting the unlabeled data to understand the population structure of anatomical landmarks. The global shape constraint is the inherent property of anatomical landmarks that provides valuable guidance for more consistent pseudo labelling of the unlabeled data, which is ignored in the previously semi-supervised methods. In this paper, we propose a model-agnostic shape-regulated…
FM-OSD: Foundation Model-Enabled One-Shot Detection of Anatomical Landmarks
The Chinese University of Hong Kong（香港中文大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Massachusetts General Brigham（麻省总医院布里格姆医疗系统）；Institute of Medical Intelligence and XR, The Chinese University of Hong Kong（香港中文大学医学智能与XR研究所）
Deploying the idea of long-term cumulative return, reinforcement learning has shown remarkable performance in various fields. We propose a formulation of the landmark localization in 3D medical images as a reinforcement learning problem. Whereas value-based methods have been widely used to solve similar problems, we adopt an actor-critic based direct policy search method framed in a temporal difference learning approach. Successful behavior learning is challenging in large state and/or action spaces, requiring many trials. We introduce a partial policy-based reinforcement learning to enable so…
Accurate detection of anatomical landmarks is an essential step in several medical imaging tasks. We propose a novel communicative multi-agent reinforcement learning (C-MARL) system to automatically detect landmarks in 3D brain images. C-MARL enables the agents to learn explicit communication channels, as well as implicit communication signals by sharing certain weights of the architecture among all the agents. The proposed approach is evaluated on two brain imaging datasets from adult magnetic resonance imaging (MRI) and fetal ultrasound scans. Our experiments show that involving multiple coo…
Automatic and accurate detection of anatomical landmarks is an essential operation in medical image analysis with a multitude of applications. Recent deep learning methods have improved results by directly encoding the appearance of the captured anatomy with the likelihood maps (i.e., heatmaps). However, most current solutions overlook another essence of heatmap regression, the objective metric for regressing target heatmaps and rely on hand-crafted heuristics to set the target precision, thus being usually cumbersome and task-specific. In this paper, we propose a novel learning-to-learn frame…
MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detection
The Hong Kong University of Science and Technology（香港科技大学）；The University of Hong Kong（香港大学）；Erasmus MC（埃因霍温麦斯学院）；Medical University of Innsbruck（因斯布鲁克医科大学）；University of Barcelona（巴塞罗那大学）
Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?
MaLGa Center, DIBRIS, Università degli studi di Genova, Genoa, Italy（MaLGa中心，DIBRIS，意大利热那亚大学，热那亚，意大利）；MaLGa Center, DIMA, Università degli studi di Genova, Genoa, Italy（MaLGa中心，DIMA，意大利热那亚大学，热那亚，意大利）
University of Electronic Science and Technology of China（电子科技大学）；SenseTime Research（商汤科技研究院）
Deep learning networks have shown promising performance for accurate object localization in medial images, but require large amount of annotated data for supervised training, which is expensive and expertise burdensome. To address this problem, we present a one-shot framework for organ and landmark localization in volumetric medical images, which does not need any annotation during the training stage and could be employed to locate any landmarks or organs in test images given a support (reference) image during the inference stage. Our main idea comes from that tissues and organs from different…
The detection of anatomical landmarks is a vital step for medical image analysis and applications for diagnosis, interpretation and guidance. Manual annotation of landmarks is a tedious process that requires domain-specific expertise and introduces inter-observer variability. This paper proposes a new detection approach for multiple landmarks based on multi-agent reinforcement learning. Our hypothesis is that the position of all anatomical landmarks is interdependent and non-random within the human anatomy, thus finding one landmark can help to deduce the location of others. Using a Deep Q-Net…
UOD: Universal One-shot Detection of Anatomical Landmarks
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（生物医学工程学院，生命科学与医学系，科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（苏州先进研究院，科学技术大学）；Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS（中国科学院智能信息处理重点实验室，计算技术研究所，中国科学院）；Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences（放射科，广东省人民医院，广东省医学科学院）；Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences（广东省人工智能在医学影像分析与应用重点实验室，广东省人民医院，广东省医学科学院）
本文提出 UOD 框架，通过领域自适应卷积与 transformer 与领域共享模块，在每域仅一个标注样本的条件下实现跨头、手、胸多领域 X 射线标志点检测，并达到最先进性能。
IBM Research - Australia（IBM澳大利亚研究院）；EPF Laussane（洛桑联邦理工学院）
Anatomical landmark segmentation and pathology localization are important steps in automated analysis of medical images. They are particularly challenging when the anatomy or pathology is small, as in retinal images and cardiac MRI, or when the image is of low quality due to device acquisition parameters as in magnetic resonance (MR) scanners. We propose an image super-resolution method using progressive generative adversarial networks (P-GAN) that can take as input a low-resolution image and generate a high resolution image of desired scaling factor. The super resolved images can be used for …
University of Science and Technology of China（中国科学技术大学）
Accurate anatomical landmark detection plays an increasingly vital role in medical image analysis. Although existing methods achieve satisfying performance, they are mostly based on CNN and specialized for a single domain say associated with a particular anatomical region. In this work, we propose a universal model for multi-domain landmark detection by taking advantage of transformer for modeling long dependencies and develop a domain-adaptive transformer model, named as DATR, which is trained on multiple mixed datasets from different anatomies and capable of detecting landmarks of any image …
The Chinese University of Hong Kong（香港中文大学）；Massachusetts Institute of Technology（麻省理工学院）
In medical imaging, surface registration is extensively used for performing systematic comparisons between anatomical structures, with a prime example being the highly convoluted brain cortical surfaces. To obtain a meaningful registration, a common approach is to identify prominent features on the surfaces and establish a low-distortion mapping between them with the feature correspondence encoded as landmark constraints. Prior registration works have primarily focused on using manually labeled landmarks and solving highly nonlinear optimization problems, which are time-consuming and hence hin…
Landmark matching via geodesic shooting is a prerequisite task for numerous registration based applications in biomedicine. Geodesic shooting has been developed as one solution approach and formulates the diffeomorphic registration as an optimal control problem under the Hamiltonian framework. In this framework, with landmark positions q0 fixed, the problem solely depends on the initial momentum p0 and evolves through time steps according to a set of constraint equations. Given an initial p0, the algorithm flows q and p forward through time steps, calculates a loss based on point-set mismatch …
MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection
College of Information and Engineering, Capital Normal University（首都师范大学信息工程学院）；Peking University Third Hospital（北京大学第三医院）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）
Detecting anatomical landmarks in medical images plays an essential role in understanding the anatomy and planning automated processing. In recent years, a variety of deep neural network methods have been developed to detect landmarks automatically. However, all of those methods are unary in the sense that a highly specialized network is trained for a single task say associated with a particular anatomical region. In this work, for the first time, we investigate the idea of "You Only Learn Once (YOLO)" and develop a universal anatomical landmark detection model to realize multiple landmark det…
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images
Shanghai Academy of AI for Science University of Queensland（上海人工智能科学研究院 华南理工大学）；Fudan University Shanghai Academy of AI for Science（复旦大学 上海人工智能科学研究院）；University of Queensland（昆士兰大学）
Few-shot medical image classification with simple shape and texture text descriptors using vision-language models
RIKEN Center for Brain Science（理化学研究所脑科学中心）；Institute of Fundamental Technological Research, Polish Academy of Sciences（波兰科学院基础技术研究所）；Faculty of Computer Science, Universitas Indonesia（印度尼西亚大学计算机学院）
Although having achieved great success in medical image segmentation, deep convolutional neural networks usually require a large dataset with manual annotations for training and are difficult to generalize to unseen classes. Few-shot learning has the potential to address these challenges by learning new classes from only a few labeled examples. In this work, we propose a new framework for few-shot medical image segmentation based on prototypical networks. Our innovation lies in the design of two key modules: 1) a context relation encoder (CRE) that uses correlation to capture local relation fe…
DiffuSAM: Diffusion-Based Prompt-Free SAM2 for Few-Shot and Source-Free Medical Image Segmentation
School of Electrical Engineering, Tel Aviv University, Israel（特拉维夫大学电气工程学院）；School of Biomedical Engineering, Tel Aviv University, Israel（特拉维夫大学生物医学工程学院）
HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection
Suzhou Institute for Advanced Research, University of Science and Technology of China (USTC)（中国科学技术大学苏州高等研究院）；School of Computer Science and Technology, University of Science and Technology of China (USTC)（中国科学技术大学计算机科学与技术学院）；School of Information Science and Technology, Eastern Institute of Technology (EIT)（宁波东方理工大学信息科学与技术学院）；Stanford University（斯坦福大学）
Image segmentation is an important task in many medical applications. Methods based on convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on supervised training with large labeled datasets. Labeling medical images requires significant expertise and time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. We present an automated data augmentation method for synthesizing labeled medical images. We demonstrate our method on the task of segmenting magnetic resonance imaging (MRI) brain scans. Our …
Xiamen University（厦门大学）；Chinese PLA General Hospital（中国人民解放军总医院）；Deepwise AI Laboratory（深智云AI实验室）
Despite deep convolutional neural networks achieved impressive progress in medical image computing and analysis, its paradigm of supervised learning demands a large number of annotations for training to avoid overfitting and achieving promising results. In clinical practices, massive semantic annotations are difficult to acquire in some conditions where specialized biomedical expert knowledge is required, and it is also a common condition where only few annotated classes are available. In this work, we proposed a novel method for few-shot medical image segmentation, which enables a segmentatio…
Are Video Models Emerging as Zero-Shot Learners and Reasoners in Medical Imaging?
Department of Computer Science, Emory University（埃默里大学计算机科学系）；Department of Computer Science, University of Southern California（南加州大学计算机科学系）；Department of Computer Science, University of Maryland（马里兰大学计算机科学系）；Department of Biomedical Engineering, Georgia Institute of Technology（佐治亚理工学院生物医学工程系）；Department of Radiation Oncology and Winship Cancer Institute, Emory University（埃默里大学放射肿瘤学系和Winship癌症研究所）
University of Freiburg（弗莱堡大学）；German Cancer Consortium (DKTK)（德国癌症联盟（DKTK））
Deformable image registration is a very important field of research in medical imaging. Recently multiple deep learning approaches were published in this area showing promising results. However, drawbacks of deep learning methods are the need for a large amount of training datasets and their inability to register unseen images different from the training datasets. One shot learning comes without the need of large training datasets and has already been proven to be applicable to 3D data. In this work we present a one shot registration approach for periodic motion tracking in 3D and 4D datasets.…
Peking University（北京大学）；Deepwise AI Lab（深睿医疗人工智能实验室）；Microsoft Research Asia（微软亚洲研究院）；The University of Hong Kong（香港大学）
As an important upstream task for many medical applications, supervised landmark localization still requires non-negligible annotation costs to achieve desirable performance. Besides, due to cumbersome collection procedures, the limited size of medical landmark datasets impacts the effectiveness of large-scale self-supervised pre-training methods. To address these challenges, we propose a two-stage framework for one-shot medical landmark localization, which first infers landmarks by unsupervised registration from the labeled exemplar to unlabeled targets, and then utilizes these noisy pseudo l…
Fudan University（复旦大学）；University of Oxford（牛津大学）；Shanghai Jiao Tong University（上海交通大学）；University of Chinese Academy of Sciences（中国科学院大学）；Huashan Hospital, Fudan University（复旦大学附属华山医院）；Zhong Shan Hospital, Fudan University（复旦大学附属中山医院）
Medical images are generally acquired with limited field-of-view (FOV), which could lead to incomplete regions of interest (ROI), and thus impose a great challenge on medical image analysis. This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets. Based on learning a navigation policy, instead of predicting targets directly, reinforcement learning (RL)-based methods have the potential totackle this challenge in an efficient ma…
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging
Rice University（莱斯大学）；McGovern Medical School, UTHealth（德克萨斯大学健康科学中心麦戈文医学院）；The University of Maryland Medical Intelligent Imaging (UM2ii) Center（马里兰大学医学智能成像中心）
UiT The Arctic University of Norway（特罗姆瑟大学（挪威北极圈大学））
Recent work has shown that label-efficient few-shot learning through self-supervision can achieve promising medical image segmentation results. However, few-shot segmentation models typically rely on prototype representations of the semantic classes, resulting in a loss of local information that can degrade performance. This is particularly problematic for the typically large and highly heterogeneous background class in medical image segmentation problems. Previous works have attempted to address this issue by learning additional prototypes for each class, but since the prototypes are based on…
In this paper, we propose a novel approach for few-shot semantic segmentation with sparse labeled images. We investigate the effectiveness of our method, which is based on the Model-Agnostic Meta-Learning (MAML) algorithm, in the medical scenario, where the use of sparse labeling and few-shot can alleviate the cost of producing new annotated datasets. Our method uses sparse labels in the meta-training and dense labels in the meta-test, thus making the model learn to predict dense labels from sparse ones. We conducted experiments with four Chest X-Ray datasets to evaluate two types of annotatio…
A Systematic Review of Few-Shot Learning in Medical Imaging
Institute of Information Science and Technologies “Alessandro Faedo”, National Research Council of Italy (ISTI-CNR)（意大利国家研究委员会信息科学与技术研究所）；Department of Information Engineering, University of Pisa（比萨大学信息工程系）
A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis
Zhejiang University-University of Illinois at Urbana-Champaign Institute（浙江大学伊利诺伊大学厄巴纳香槟校区联合学院）；Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；Angelalign Inc.（时代天使公司）
Fudan University（复旦大学）；Shanghai Key Laboratory of Intelligent Information Processing（上海市智能信息处理重点实验室）；School of Computer Science, Fudan University（复旦大学计算机科学学院）
Heatmap-based methods play an important role in anatomical landmark detection. However, most current heatmap-based methods assume that the distributions of all landmarks are the same and the distribution of each landmark is isotropic, which may not be in line with reality. For example, the landmark on the jaw is more likely to be located along the edge and less likely to be located inside or outside the jaw. Manually annotating tends to follow similar rules, resulting in an anisotropic distribution for annotated landmarks, which represents the uncertainty in the annotation. To estimate the unc…
Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging
Cluster of Excellence “Machine Learning”（卓越集群“机器学习”）；University of Tübingen（蒂宾根大学）；Faculty of Health Sciences and Medicine, University of Lucerne（卢塞恩大学健康科学与医学院）
