Interactive segmentation is a promising strategy for building robust, generalisable algorithms for volumetric medical image segmentation. However, inconsistent and clinically unrealistic evaluation hinders fair comparison and misrepresents real-world performance. We propose a clinically grounded methodology for defining evaluation tasks and metrics, and built a software framework for constructing standardised evaluation pipelines. We evaluate state-of-the-art algorithms across heterogeneous and complex tasks and observe that (i) minimising information loss when processing user interactions is critical for model robustness, (ii) adaptive-zooming mechanisms boost robustness and speed convergence, (iii) performance drops if validation prompting behaviour/budgets differ from training, (iv) 2D methods perform well with slab-like images and coarse targets, but 3D context helps with large or irregularly shaped targets, (v) performance of non-medical-domain models (e.g. SAM2) degrades with poor contrast and complex shapes.
Interactive Image Selection and Training for Brain Tumor Segmentation Network
Institute of Computing, University of Campinas（坎皮纳斯大学计算研究所）；Hospital de Clínicas, Universidade Federal do Paraná（巴拉那联邦大学临床医院）；Instituto de Neurologia de Curitiba（库里蒂巴神经研究所）
VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathology
Department of Pathology and Laboratory Medicine, University of Pennsylvania（病理学与实验室医学系，宾夕法尼亚大学）；Department of Electrical and System Engineering, University of Pennsylvania（电气与系统工程系，宾夕法尼亚大学）；Department of Biomedical Engineering, Georgia Institute of Technology and Emory University（生物医学工程系，佐治亚理工学院和埃默里大学）；NVIDIA Corporation（NVIDIA公司）；Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania（生物统计学、流行病学与信息学系，宾夕法尼亚大学）
ENS, PSL University & INRIA（巴黎高等师范学院、巴黎文理研究大学与法国国家信息与自动化研究所）；ENS, CNRS, PSL University（巴黎高等师范学院、法国国家科学研究中心与巴黎文理研究大学）；ENS Paris-Saclay（巴黎萨克雷高等师范学院）
We present interactive painting processes in which a painter and various neural style transfer algorithms interact on a real canvas. Understanding what these algorithms' outputs achieve is then paramount to describe the creative agency in our interactive experiments. We gather a set of paired painting-pictures images and present a new evaluation methodology based on the predictivity of neural style transfer algorithms. We point some algorithms' instabilities and show that they can be used to enlarge the diversity and pleasing oddity of the images synthesized by the numerous existing neural sty…
Hong Kong Baptist University（香港浸会大学）；Queen Elizabeth and Kwong Wah Hospitals（伊利沙伯医院及广华医院）；Tuen Mun Hospital（屯门医院）；NVIDIA Corporation（英伟达公司）
Automatic generation of medical reports from X-ray images can assist radiologists to perform the time-consuming and yet important reporting task. Yet, achieving clinically accurate generated reports remains challenging. Modeling the underlying abnormalities using the knowledge graph approach has been found promising in enhancing the clinical accuracy. In this paper, we introduce a novel fined-grained knowledge graph structure called an attributed abnormality graph (ATAG). The ATAG consists of interconnected abnormality nodes and attribute nodes, allowing it to better capture the abnormality de…
University of Electronic Science and Technology of China（电子科技大学）；SenseTime Research（商汤科技研究院）；Shanghai Jiao Tong University（上海交通大学）；University Hospitals Leuven（鲁汶大学医院）；University College London（伦敦大学学院）；King’s College London（伦敦国王学院）
Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Networks (CNN) have achieved the state-of-the-art performance for automatic segmentation, they are often limited by the lack of clinically acceptable accuracy and robustness in complex cases. Therefore, interactive segmentation is a practical alternative to these methods. However, traditional interactive segmentation methods require a large amount of user interactions, and recently proposed CNN-based interactive segmen…
Prompt-Guided Interactive Segmentation of Interstitial Lung Disease in Thoracic CT
University of Bern（伯尔尼大学）；Graduate School for Cellular and Biomedical Sciences（细胞与生物医学科学研究生院）；Bern University Hospital（伯尔尼大学医院）；Lucern Cantonal Hospital（卢塞恩州立医院）；Lausanne University Hospital (CHUV) and University of Lausanne（洛桑大学医院（CHUV）与洛桑大学）
Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking
German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany（德国癌症研究中心（DKFZ）海德堡，医学图像计算部，德国）；Faculty of Mathematics and Computer Science, Heidelberg University, Germany（海德堡大学数学与计算机科学学院，德国）；HIDSS4Health -- Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany（HIDSS4Health——海德堡信息与数据科学健康学校，卡尔斯鲁厄/海德堡，德国）；Medical Faculty, Heidelberg University, Germany（海德堡大学医学学院，德国）；University Hospital Brandenburg an der Havel, Brandenburg Medical School Theodor Fontane, Germany（勃兰登堡运河大学医院，布兰登堡泰奥多尔·冯·_fontane医学学校，德国）；Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Germany（放射肿瘤科模式分析与学习组，海德堡大学医院，德国）
Technical University of Munich（慕尼黑工业大学）；TU Graz（格拉茨工业大学）；BioTechMed（生物技术医学）
Among all types of cancer, gynecological malignancies belong to the 4th most frequent type of cancer among women. Besides chemotherapy and external beam radiation, brachytherapy is the standard procedure for the treatment of these malignancies. In the progress of treatment planning, localization of the tumor as the target volume and adjacent organs of risks by segmentation is crucial to accomplish an optimal radiation distribution to the tumor while simultaneously preserving healthy tissue. Segmentation is performed manually and represents a time-consuming task in clinical daily routine. This …
Johns Hopkins University（约翰斯·霍普金斯大学）；Department of Computer Science, Johns Hopkins University（约翰斯·霍普金斯大学计算机科学系）；Laboratory for Computer Aided Medical Procedures, Johns Hopkins University（约翰斯·霍普金斯大学计算机辅助医疗程序实验室）
Robot-assisted minimally invasive surgery has shown to improve patient outcomes, as well as reduce complications and recovery time for several clinical applications. While increasingly configurable robotic arms can maximize reach and avoid collisions in cluttered environments, positioning them appropriately during surgery is complicated because safety regulations prevent automatic driving. We propose a head-mounted display (HMD) based augmented reality (AR) system designed to guide optimal surgical arm set up. The staff equipped with HMD aligns the robot with its planned virtual counterpart. I…
