Exploiting Partial Structural Symmetry For Patient-Specific Image Augmentation in Trauma Interventions
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Hospital（约翰斯·霍普金斯医院）；Technische Universität München（慕尼黑工业大学）
Javad Fotouhi, Mathias Unberath, Giacomo Taylor, Arash Ghaani Farashahi, Bastian Bier, Russell H. Taylor, Greg M. Osgood, M. D., Mehran Armand, Nassir Navab
In unilateral pelvic fracture reductions, surgeons attempt to reconstruct the bone fragments such that bilateral symmetry in the bony anatomy is restored. We propose to exploit this "structurally symmetric" nature of the pelvic bone, and provide intra-operative image augmentation to assist the surgeon in repairing dislocated fragments. The main challenge is to automatically estimate the desired plane of symmetry within the patient's pre-operative CT. We propose to estimate this plane using a non-linear optimization strategy, by minimizing Tukey's biweight robust estimator, relying on the partial symmetry of the anatomy. Moreover, a regularization term is designed to enforce the similarity of bone density histograms on both sides of this plane, relying on the biological fact that, even if injured, the dislocated bone segments remain within the body. The experimental results demonstrate the performance of the proposed method in estimating this "plane of partial symmetry" using CT images of both healthy and injured anatomy. Examples of unilateral pelvic fractures are used to show how intra-operative X-ray images could be augmented with the forward-projections of the mirrored anatomy, acting as objective road-map for fracture reduction procedures.
Shanghai Jiao Tong University（上海交通大学）；Huashan Hospital, Shanghai Medical College, Fudan University（复旦大学上海医学院华山医院）；National Center for Neurological Disorders（国家神经疾病中心）；Fudan University（复旦大学）；ShanghaiTech University（上海科技大学）
Brain network analysis for traumatic brain injury (TBI) patients is critical for its consciousness level assessment and prognosis evaluation, which requires the segmentation of certain consciousness-related brain regions. However, it is difficult to construct a TBI segmentation model as manually annotated MR scans of TBI patients are hard to collect. Data augmentation techniques can be applied to alleviate the issue of data scarcity. However, conventional data augmentation strategies such as spatial and intensity transformation are unable to mimic the deformation and lesions in traumatic brain…
We propose a novel framework for controllable pathological image synthesis for data augmentation. Inspired by CycleGAN, we perform cycle-consistent image-to-image translation between two domains: healthy and pathological. Guided by a semantic mask, an adversarially trained generator synthesizes pathology on a healthy image in the specified location. We demonstrate our approach on an institutional dataset of cerebral microbleeds in traumatic brain injury patients. We utilize synthetic images generated with our method for data augmentation in cerebral microbleeds detection. Enriching the trainin…
YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images
Department of Electrical and Computer Engineering, Tamkang University（潭府大学电气与计算机工程系）；Graduate Institute of Networking and Multimedia, National Taiwan University（台湾大学网络与多媒体研究所）；School of Nursing, National Taipei University of Nursing and Health Sciences（台北护理大学护理学院）
University of Amsterdam（阿姆斯特丹大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
Machine learning models trained with purely observational data and the principle of empirical risk minimization \citep{vapnik_principles_1992} can fail to generalize to unseen domains. In this paper, we focus on the case where the problem arises through spurious correlation between the observed domains and the actual task labels. We find that many domain generalization methods do not explicitly take this spurious correlation into account. Instead, especially in more application-oriented research areas like medical imaging or robotics, data augmentation techniques that are based on heuristics a…
The Johns Hopkins University（约翰斯·霍普金斯大学）；University of Maryland（马里兰大学）；R. Adams Cowley Shock Trauma Center（R·亚当斯·考利休克创伤中心）
Trauma is the worldwide leading cause of death and disability in those younger than 45 years, and pelvic fractures are a major source of morbidity and mortality. Automated segmentation of multiple foci of arterial bleeding from abdominopelvic trauma CT could provide rapid objective measurements of the total extent of active bleeding, potentially augmenting outcome prediction at the point of care, while improving patient triage, allocation of appropriate resources, and time to definitive intervention. In spite of the importance of active bleeding in the quick tempo of trauma care, the task is s…
Technion - Israel Institute of Technology（以色列理工学院）
Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal they are being fed with. To understand and better measure the generalization capabilities of VQA systems, we look at their robustness to counterfactually augmented data. Our proposed augmentations are designed to make a focused intervention on a specific property of the question such that the answer changes. Using these augmentations, we propose a new robustness measure, Robustness to Augmented Data (RAD), which measur…
Technische Universität München（慕尼黑工业大学）；Sharif University of Technology（谢里夫理工大学）；John Hopkins University（约翰霍普金斯大学）
Medical Ultrasound (US), despite its wide use, is characterized by artifacts and operator dependency. Those attributes hinder the gathering and utilization of US datasets for the training of Deep Neural Networks used for Computer-Assisted Intervention Systems. Data augmentation is commonly used to enhance model generalization and performance. However, common data augmentation techniques, such as affine transformations do not align with the physics of US and, when used carelessly can lead to unrealistic US images. To this end, we propose a set of physics-inspired transformations, including defo…
JD Finance America Corporation（京东金融美国公司）；University of Pittsburgh（匹兹堡大学）
It is challenging to train a robust object detector under the supervised learning setting when the annotated data are scarce. Thus, previous approaches tackling this problem are in two categories: semi-supervised learning models that interpolate labeled data from unlabeled data, and self-supervised learning approaches that exploit signals within unlabeled data via pretext tasks. To seamlessly integrate and enhance existing supervised object detection methods, in this work, we focus on addressing the data scarcity problem from a fundamental viewpoint without changing the supervised learning par…
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Hospital（约翰斯·霍普金斯医院）；Technical University of Munich（慕尼黑工业大学）
We present a novel methodology to detect imperfect bilateral symmetry in CT of human anatomy. In this paper, the structurally symmetric nature of the pelvic bone is explored and is used to provide interventional image augmentation for treatment of unilateral fractures in patients with traumatic injuries. The mathematical basis of our solution is on the incorporation of attributes and characteristics that satisfy the properties of intrinsic and extrinsic symmetry and are robust to outliers. In the first step, feature points that satisfy intrinsic symmetry are automatically detected in the Möbiu…
Image classification models often learn to predict a class based on irrelevant co-occurrences between input features and an output class in training data. We call the unwanted correlations "data biases," and the visual features causing data biases "bias factors." It is challenging to identify and mitigate biases automatically without human intervention. Therefore, we conducted a design study to find a human-in-the-loop solution. First, we identified user tasks that capture the bias mitigation process for image classification models with three experts. Then, to support the tasks, we developed a…
University of Toronto Robotics Institute（多伦多大学机器人研究所）
The robustness of visual navigation policies trained through imitation often hinges on the augmentation of the training image-action pairs. Traditionally, this has been done by collecting data from multiple cameras, by using standard data augmentations from computer vision, such as adding random noise to each image, or by synthesizing training images. In this paper we show that there is another practical alternative for data augmentation for visual navigation based on extrapolating viewpoint embeddings and actions nearby the ones observed in the training data. Our method makes use of the geome…
Improving Quality Control of Whole Slide Images by Explicit Artifact Augmentation
AGH University of Krakow（克拉科夫AGH科技大学）；University of Applied Sciences Western Switzerland (HES-SO)（瑞士西部应用科学大学（HES-SO））；Radboud University Medical Center（拉德堡德大学医学中心）；University of Padova（帕多瓦大学）；University of Geneva（日内瓦大学）
SegMatch: A semi-supervised learning method for surgical instrument segmentation
School of Biomedical Engineering & Imaging Sciences, King’s College London（生物医学工程与影像科学学院，伦敦国王学院）；Harvard Medical School, Harvard University（哈佛医学院，哈佛大学）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）
Deep learning using neural networks has provided advances in image style transfer, merging the content of one image (e.g., a photo) with the style of another (e.g., a painting). Our research shows this concept can be extended to analyse the design of streetscapes in relation to health and wellbeing outcomes. An Australian population health survey (n=34,000) was used to identify the spatial distribution of health and wellbeing outcomes, including general health and social capital. For each outcome, the most and least desirable locations formed two domains. Streetscape design was sampled using a…
Tumor-aware augmentation with task-guided attention analysis improves rectal cancer segmentation from magnetic resonance images
Department of Medical Physics, Memorial Sloan Kettering Cancer Center（医学物理系，纪念斯隆凯特琳癌症中心）；Department of Radiology, Memorial Sloan Kettering Cancer Center（放射学系，纪念斯隆凯特琳癌症中心）；Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center（放射肿瘤学系，纪念斯隆凯特琳癌症中心）；Department of Surgery, Memorial Sloan Kettering Cancer Center（外科系，纪念斯隆凯特琳癌症中心）；Department of Medicine, Memorial Sloan Kettering Cancer Center（医学系，纪念斯隆凯特琳癌症中心）
Fluoroscopic imaging that captures X-ray images at video framerates is advantageous for guiding catheter insertions by vascular surgeons and interventional radiologists. Visualizing the dynamical movements non-invasively allows complex surgical procedures to be performed with less trauma to the patient. To improve surgical precision, endovascular procedures can benefit from more accurate fluoroscopy data via calibration. This paper presents a robust self-calibration algorithm suitable for single-plane and dual-plane fluoroscopy. A three-dimensional (3D) target field was imaged by the fluorosco…
