Accurate and robust ultrasound image segmentation is critical for computer-aided diagnostic systems. Nevertheless, the inherent challenges of ultrasound imaging, such as blurry boundaries and speckle noise, often cause traditional segmentation methods to struggle with performance. Despite recent advancements in universal image segmentation, such as the Segment Anything Model, existing interactive segmentation methods still suffer from inefficiency and lack of specialization. These methods rely heavily on extensive accurate manual or random sampling prompts for interaction, necessitating numerous prompts and iterations to reach satisfactory performance. In response to this challenge, we propose the Evidential Uncertainty-Guided Interactive Segmentation (EUGIS), an end-to-end, efficient tiered interactive segmentation paradigm based on evidential uncertainty estimation for ultrasound image segmentation. Specifically, EUGIS harnesses evidence-based uncertainty estimation, grounded in Dempster-Shafer theory and Subjective Logic, to gauge the level of uncertainty in the predictions of model for different regions. By prioritizing sampling the high-uncertainty region, our method can effectively simulate the interactive behavior of well-trained radiologists, enhancing the targeted of sampling while reducing the number of prompts and iterations required.Additionally, we propose a trainable calibration mechanism for uncertainty estimation, which can further optimize the boundary between certainty and uncertainty, thereby enhancing the confidence of uncertainty estimation.
Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty
National Key Laboratory of Fundamental Science on Synthetic Vision, the College of Computer Science, Sichuan University（国家基础科学合成视觉重点实验室、计算机科学学院、四川大学）；Department of Radiology, West China Hospital, Sichuan University（放射科、西昌医院、四川大学）；Saw Swee Hock School of Public Health, National University of Singapore（Saw Swee Hock 公共卫生学院、新加坡国立大学）；Department of Mathematics, Sichuan University（数学系、四川大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（高性能计算研究所（IHPC）、科学、技术与研究局（A*STAR））；Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore（创新与精准眼健康中心、 Yong Loo Lin 医学院、新加坡国立大学）；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡眼研究所、新加坡国家眼科中心）
Queen’s University（女王大学）；University of British Columbia（不列颠哥伦比亚大学）；Exact Imaging
MOTIVATION: Detection of prostate cancer during transrectal ultrasound-guided biopsy is challenging. The highly heterogeneous appearance of cancer, presence of ultrasound artefacts, and noise all contribute to these difficulties. Recent advancements in high-frequency ultrasound imaging - micro-ultrasound - have drastically increased the capability of tissue imaging at high resolution. Our aim is to investigate the development of a robust deep learning model specifically for micro-ultrasound-guided prostate cancer biopsy. For the model to be clinically adopted, a key challenge is to design a so…
A Controlled Audit of Architectural Complexity in Uncertainty-Aware Multi-Organ Ultrasound Classification
The Hong Kong Polytechnic University（香港理工大学）；Beihang University（北京航空航天大学）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Shandong University（山东大学）
BAAF: A benchmark attention adaptive framework for medical ultrasound image segmentation tasks
College of Biomedical Engineering and Technology, Tianjin Medical University（天津医科大学生物医学工程学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；Department of Urology, Fourth Medical Center of PLA General Hospital（中国人民解放军第四医学中心泌尿外科科）；Department of Urology, Civil Aviation General Hospital（民航总医院泌尿外科科）；College of Artificial Intelligence, Nankai University（南开大学人工智能学院）；Department of Ultrasonography, Tianjin Medical University Second Hospital（天津医科大学第二医院超声科）
EviRCOD: Evidence-Guided Probabilistic Decoding for Referring Camouflaged Object Detection
West China Hospital, Sichuan University（四川大学华西医院）；Independent Researcher（独立研究员）；Department of Data Science and Artificial Intelligence, Auckland University of Technology（奥克兰理工大学数据科学与人工智能系）
Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review
Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg（卢森堡大学生命科学与医学系，科学、技术与医学学院）；Department of Biostatistics and Medical Informatics, Institute of Health Sciences, Ege University（伊兹密尔大学健康科学学院生物统计学与医学信息学系）
Calibrated Selective Prediction Using Deep Ensembles for ROI-Based Thyroid Nodule Ultrasound Classification Under Dataset Shift: A Retrospective Evaluation
Daffodil International University（达福德国际大学）；Birmingham City University（伯明翰城市大学）
Institute of Research and Technology b<>com（b<>com技术研究院）
Purpose. Ability to locate and track ultrasound images in the 3D operating space is of great benefit for multiple clinical applications. This is often accomplished by tracking the probe using a precise but expensive optical or electromagnetic tracking system. Our goal is to develop a simple and low cost augmented reality echography framework using a standard RGB-D Camera. Methods. A prototype system consisting of an Occipital Structure Core RGB-D camera, a specifically-designed 3D marker, and a fast point cloud registration algorithm FaVoR was developed and evaluated on an Ultrasonix ultrasoun…
UltrasODM: A Dual Stream Optical Flow Mamba Network for 3D Freehand Ultrasound Reconstruction
Department of Information Technology（信息科技系）；Indian Institute of Information Technology Allahabad Prayagraj（阿拉哈巴德普拉亚格拉杰信息科技学院）；Department of Electronics & Communication Engineering（电子与通信工程系）；Department of Computer Engineering and Mathematics（计算机工程与数学系）；Universitat Rovira i Virgili（罗维拉-维格利大学）
Centre for Medical Image Computing, University College London（伦敦大学学院医学图像计算中心）；Institute of Biomedical Engineering, University of Oxford（牛津大学生物医学工程研究所）；Department of Diagnostic Imaging, Royal Free London NHS Foundation Trust（伦敦皇家自由NHS基金会信托诊断影像科）
Sonography synthesis has a wide range of applications, including medical procedure simulation, clinical training and multimodality image registration. In this paper, we propose a machine learning approach to simulate ultrasound images at given 3D spatial locations (relative to the patient anatomy), based on conditional generative adversarial networks (GANs). In particular, we introduce a novel neural network architecture that can sample anatomically accurate images conditionally on spatial position of the (real or mock) freehand ultrasound probe. To ensure an effective and efficient spatial in…
Uncertainty-Aware Ordinal Deep Learning for cross-Dataset Diabetic Retinopathy Grading
Department of Computer Science & Engineering, Mississippi State University, MS, USA（计算机科学与工程系，密苏里州立大学）；Department of Mechanical Engineering, Mississippi State University, MS, USA（机械工程系，密苏里州立大学）；College of Engineering & Architecture, International University of Rabat（工程与建筑学院，拉巴特国际大学）
Towards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation
Department of Computer Engineering, Kwame Nkrumah University of Science and Technology（计算机工程系，加纳科拉布大学科学与技术大学）；Global Health and Infectious Disease Group, Kumasi Centre for Collaborative Research in Tropical Medicine（全球卫生与传染病组，科拉布热带医学协作研究中心）；Department of Radiology, National Hospital Abuja（放射科，阿布贾国家医院）；Computer Science Department, FAST National University of Computer and Emerging Sciences（计算机科学系，FAST国家计算机与新兴科学大学）；Medical Artificial Intelligence Lab, Lagos, Nigeria（医学人工智能实验室，尼日利亚拉各斯）；Department of Biomedical Engineering, McGill University（生物医学工程系，麦吉尔大学）；Perelman School of Medicine, University of Pennsylvania（佩尔曼医学学院，宾夕法尼亚大学）；Nnamdi Azikiwe University Teaching Hospital（纳曼迪·阿齐基韦大学教学医院）
Baidu Inc.（百度公司）；University of Electronic Science and Technology of China（电子科技大学）；South China University of Technology（华南理工大学）；Shenzhen Eye Hospital, Jinan University（暨南大学附属深圳眼科医院）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）
On the medical images, many of the tissues/lesions may be ambiguous. That is why the medical segmentation is typically annotated by a group of clinical experts to mitigate the personal bias. However, this clinical routine also brings new challenges to the application of machine learning algorithms. Without a definite ground-truth, it will be difficult to train and evaluate the deep learning models. When the annotations are collected from different graders, a common choice is majority vote. However such a strategy ignores the difference between the grader expertness. In this paper, we consider …
