Structure-Guided Self-Supervised Matching for One-Shot Medical Landmark Detection
Department of Computer Science（计算机科学系）；Renmin University of China（中国人民大学）；University of Science and Technology of China（中国科学技术大学）；School of Biomedical Engineering（生物医学工程学院）；School of Artificial Intelligence（人工智能学院）；Shanghai Jiaotong University（上海交通大学）；Department of Orthopedics（骨科系）；Affiliated Hospital of Guizhou Medical University（贵州医科大学附属医院）；Yale University（耶鲁大学）
Qingsong Yao, Zhen Huang, Ao Wang, Rongsheng Wang, Hanxue Zhang, Jiangji Wang, S. Kevin Zhou
Medical landmark detection usually requires accurate expert annotations, which are laborious and difficult to scale across anatomical regions. In this work, we study an extreme annotation-efficient setting where only a single annotated template image is available. We propose SGB-Match, a structure-guided coarse-to-fine self-supervised matching framework for one-shot medical landmark detection. The framework first learns dense anatomical correspondence from unlabeled augmented image pairs, and then transfers the landmark definition from the annotated template to each target image through feature matching. Different from standard contrastive correspondence learning, where negative candidates are penalized by a structure-agnostic rule, we introduce a structure-guided bias into the contrastive objective. The bias is constructed from relative distance and edge-aware anatomical cues, and explicitly reweights the negative gradients: nearby structure-relevant candidates are weakly repelled, while distant or structure-irrelevant negatives are strongly suppressed. As a result, the learned feature space better preserves local anatomical structures around template landmarks and reduces confusing responses from repeated textures. We further adopt a global-to-local design, where a global encoder provides coarse landmark localization and a local encoder refines the prediction in a cropped region. Extensive experiments on four 2D radiological landmark datasets demonstrate that SGB-Match achieves strong one-shot performance across both public and newly collected datasets and consistently benefits from both structure-guided bias and two-stage refinement.
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…
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）
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…
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-t…
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…
Landmark Localization plays a very important role in processing medical images as well as in disease identification. However, In medical field, it's a challenging task because of the complexity of medical images and the high requirement of accuracy for disease identification and treatment.There are two dominant ways to regress landmark coordination, one using the full convolutional network to regress the heatmaps of landmarks , which is a complex way and heatmap post-process strategies are needed, and the other way is to regress the coordination using CNN + Full Connective Network directly, wh…
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…
This paper proposes a CNN cascade for semantic part segmentation guided by pose-specific information encoded in terms of a set of landmarks (or keypoints). There is large amount of prior work on each of these tasks separately, yet, to the best of our knowledge, this is the first time in literature that the interplay between pose estimation and semantic part segmentation is investigated. To address this limitation of prior work, in this paper, we propose a CNN cascade of tasks that firstly performs landmark localisation and then uses this information as input for guiding semantic part segmentat…
University of Trento（特伦托大学）；EPFL（洛桑联邦理工学院）；Inria（法国国家信息与自动化研究所）；Grenoble-Alpes University（格勒诺布尔阿尔卑斯大学）；Fondazione Bruno Kessler (FBK)（布鲁诺·凯斯勒基金会）
Each smile is unique: one person surely smiles in different ways (e.g., closing/opening the eyes or mouth). Given one input image of a neutral face, can we generate multiple smile videos with distinctive characteristics? To tackle this one-to-many video generation problem, we propose a novel deep learning architecture named Conditional Multi-Mode Network (CMM-Net). To better encode the dynamics of facial expressions, CMM-Net explicitly exploits facial landmarks for generating smile sequences. Specifically, a variational auto-encoder is used to learn a facial landmark embedding. This single emb…
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）
We propose a simple yet powerful Landmark guided Generative Adversarial Network (LandmarkGAN) for the facial expression-to-expression translation using a single image, which is an important and challenging task in computer vision since the expression-to-expression translation is a non-linear and non-aligned problem. Moreover, it requires a high-level semantic understanding between the input and output images since the objects in images can have arbitrary poses, sizes, locations, backgrounds, and self-occlusions. To tackle this problem, we propose utilizing facial landmark information explicitl…
Tianjin University（天津大学）；Wuhan University（武汉大学）；Tencent AI Lab（腾讯人工智能实验室）；Stony Brook University（石溪大学）
It is challenging to inpaint face images in the wild, due to the large variation of appearance, such as different poses, expressions and occlusions. A good inpainting algorithm should guarantee the realism of output, including the topological structure among eyes, nose and mouth, as well as the attribute consistency on pose, gender, ethnicity, expression, etc. This paper studies an effective deep learning based strategy to deal with these issues, which comprises of a facial landmark predicting subnet and an image inpainting subnet. Concretely, given partial observation, the landmark predictor …
Queensland University of Technology（昆士兰科技大学）；The University of Queensland（昆士兰大学）
Re-identification of individual animals in images can be ambiguous due to subtle variations in body markings between different individuals and no constraints on the poses of animals in the wild. Person re-identification is a similar task and it has been approached with a deep convolutional neural network (CNN) that learns discriminative embeddings for images of people. However, learning discriminative features for an individual animal is more challenging than for a person's appearance due to the relatively small size of ecological datasets compared to labelled datasets of person's identities. …
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…
FGTBT: Frequency-Guided Task-Balancing Transformer for Unified Facial Landmark Detection
School of Information Engineering, Zhongnan University of Economics（经济法律大学信息工程学院）；School of Computer Science and Engineering, Nanyang Technological University, Singapore（南洋理工大学计算机科学与工程学院）；College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China（深圳大学计算机科学与软件工程学院）；School of Information Science and Engineering, Yunnan University, Kunming, Yunnan, China（云南大学信息科学与工程学院）
LaCoVL-FER: Landmark-Guided Contrastive Learning Network with Vision-Language Enhancement for Facial Expression Recognition
School of Airspace and Engineering, Shandong University（山东大学航空航天与工程学院）；School of Computer Science and Technology, Shandong University of Finance and Economics（山东财经大学计算机科学与技术学院）；School of Psychology and Neuroscience, University of Glasgow（格拉斯哥大学心理学与神经科学学院）；Faculty of Information Science and Engineering, Ocean University of China（中国海洋大学信息科学与工程学院）
nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection
German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing（德国癌症研究中心（DKFZ）海德堡，医学影像计算部）；Medical Faculty Heidelberg, Heidelberg University（海德堡大学医学系）；Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital（放射肿瘤学部模式分析与学习组，海德堡大学医院）；Faculty of Mathematics and Computer Science, Heidelberg University（海德堡大学数学与计算机科学学院）；German Cancer Consortium (DKTK), DKFZ, core center Heidelberg（德国癌症联盟（DKTK），DKFZ，海德堡核心中心）；School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China（深圳大学医学院生物医学工程学院，深圳大学，深圳，广东，中国）；Helmholtz Imaging, DKFZ, Heidelberg（海德堡成像，DKFZ，海德堡）
While facial biometrics has been widely used for identification purpose, it has recently been researched as medical biometrics for a range of diseases. In this chapter, we investigate the facial landmark detection for atypical 3D facial modelling in facial palsy cases, while potentially such modelling can assist the medical diagnosis using atypical facial features. In our work, a study of landmarks localisation methods such as stacked hourglass networks is conducted and evaluated to ascertain their accuracy when presented with unseen atypical faces. The evaluation highlights that the state-of-…
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…
Indian Statistical Institute（印度统计研究所）；Institute for Datability Science, Osaka University（大阪大学数据可用性科学研究所）
In this paper, we propose a Landmark Guided Virtual Try-On (LGVTON) method for clothes, which aims to solve the problem of clothing trials on e-commerce websites. Given the images of two people: a person and a model, it generates a rendition of the person wearing the clothes of the model. This is useful considering the fact that on most e-commerce websites images of only clothes are not usually available. We follow a three-stage approach to achieve our objective. In the first stage, LGVTON warps the clothes of the model using a Thin-Plate Spline (TPS) based transformation to fit the person. Un…
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…
PAII. Inc.（PAII公司）；University of Rochester（罗切斯特大学）；University of South Carolina（南卡罗来纳大学）；Chang Gung Memorial Hospital（长庚纪念医院）；Ping An Technology（平安科技）
Image landmark detection aims to automatically identify the locations of predefined fiducial points. Despite recent success in this field, higher-ordered structural modeling to capture implicit or explicit relationships among anatomical landmarks has not been adequately exploited. In this work, we present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection. The proposed method constructs graph signals leveraging both local image features and global shape features. The adaptive graph topology naturally explores …
landmarker: a Toolkit for Anatomical Landmark Localization in 2D/3D Images
IDLab；Department of Electronics and Information Systems（电子与信息系统系）；Ghent University（根特大学）；imec；Biometrics Research Group（生物识别研究组）；Department of Morphology, Imaging, Orthopedics, Rehabilitation and Nutrition（解剖学、成像、骨科、康复和营养系）
Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware. However, recent works have empirically shown a ranking disorder between the performance of stand-alone architectures and that of the corresponding shared-weight networks. This violates the main assumption of weight-sharing NAS algorithms, thus limiting their effectiveness. We tackle this issue by proposing a regularization term that aims to maximize the correlation between the performance rankings of the shared-weight network and that of the standalone arc…
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…
Pose-Guided Self-Training with Two-Stage Clustering for Unsupervised Landmark Discovery
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Heidelberg（海德堡大学）；Information Technology University of Punjab（旁遮普信息技术大学）
Nowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images. Therefore, for super-resolving a given very low-resolution (LR) face image of a person it is very likely to find another HR face image of the same person which can be used to guide the process. In this paper, we propose a convolutional neural network (CNN)-based solution, namely GWAInet, which applies super-resolution (SR) by a factor 8x on face images guided by another unconstrained HR face image of the same person with possible differences in age, expression, pose or siz…
Generalizable Face Landmarking Guided by Conditional Face Warping
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Key Laboratory of Artificial Intelligence, Ministry of Education, Shanghai（上海 教育部人工智能重点实验室）
University of Vienna（维也纳大学）；NTNU Trondheim（挪威科技大学）
Motions of virtual characters in movies or video games are typically generated by recording actors using motion capturing methods. Animations generated this way often need postprocessing, such as improving the periodicity of cyclic animations or generating entirely new motions by interpolation of existing ones. Furthermore, search and classification of recorded motions becomes more and more important as the amount of recorded motion data grows. In this paper, we will apply methods from shape analysis to the processing of animations. More precisely, we will use the by now classical elastic metr…
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…
School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Cephalometric tracing method is usually used in orthodontic diagnosis and treatment planning. In this paper, we propose a deep learning based framework to automatically detect anatomical landmarks in cephalometric X-ray images. We train the deep encoder-decoder for landmark detection, and combine global landmark configuration with local high-resolution feature responses. The proposed frame-work is based on 2-stage u-net, regressing the multi-channel heatmaps for land-mark detection. In this framework, we embed attention mechanism with global stage heatmaps, guiding the local stage inferring, t…
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…
Imperial College London（伦敦帝国理工学院）；King’s College London（伦敦国王学院）
We propose a new Patch-based Iterative Network (PIN) for fast and accurate landmark localisation in 3D medical volumes. PIN utilises a Convolutional Neural Network (CNN) to learn the spatial relationship between an image patch and anatomical landmark positions. During inference, patches are repeatedly passed to the CNN until the estimated landmark position converges to the true landmark location. PIN is computationally efficient since the inference stage only selectively samples a small number of patches in an iterative fashion rather than a dense sampling at every location in the volume. Our …
Rethinking Medical Landmark Localization with Prototype Learning-based Progressive Offset Correction
Shenzhen University（深圳大学）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）
Canon Medical Research Europe（佳能医疗欧洲研究中心）；Queen Elizabeth University Hospital（伊丽莎白女王大学医院）；University of Glasgow（格拉斯哥大学）
We present an efficient neural network method for locating anatomical landmarks in 3D medical CT scans, using atlas location autocontext in order to learn long-range spatial context. Location predictions are made by regression to Gaussian heatmaps, one heatmap per landmark. This system allows patchwise application of a shallow network, thus enabling multiple volumetric heatmaps to be predicted concurrently without prohibitive GPU memory requirements. Further, the system allows inter-landmark spatial relationships to be exploited using a simple overdetermined affine mapping that is robust to de…
