Medical image segmentation is one of the fundamental problems for artificial intelligence-based clinical decision systems. Current automatic medical image segmentation methods are often failed to meet clinical requirements. As such, a series of interactive segmentation algorithms are proposed to utilize expert correction information. However, existing methods suffer from some segmentation refining failure problems after long-term interactions and some cost problems from expert annotation, which hinder clinical applications. This paper proposes an interactive segmentation framework, called interactive MEdical segmentation with self-adaptive Confidence CAlibration (MECCA), by introducing the corrective action evaluation, which combines the action-based confidence learning and multi-agent reinforcement learning (MARL). The evaluation is established through a novel action-based confidence network, and the corrective actions are obtained from MARL. Based on the confidential information, a self-adaptive reward function is designed to provide more detailed feedback, and a simulated label generation mechanism is proposed on unsupervised data to reduce over-reliance on labeled data. Experimental results on various medical image datasets have shown the significant performance of the proposed algorithm.
Deep learning techniques have successfully been employed in numerous computer vision tasks including image segmentation. The techniques have also been applied to medical image segmentation, one of the most critical tasks in computer-aided diagnosis. Compared with natural images, the medical image is a gray-scale image with low-contrast (even with some invisible parts). Because some organs have similar intensity and texture with neighboring organs, there is usually a need to refine automatic segmentation results. In this paper, we propose an interactive deep refinement framework to improve the …
Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
Shanghai AI Laboratory（上海人工智能实验室）；Sichuan University（四川大学）；Monash University（莫纳什大学）；East China Normal University（华东师范大学）；Shanghai Jiao Tong University（上海交通大学）；Xinjiang University（新疆大学）
Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation
East China Normal University（华东师范大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；Shanghai Jiao Tong University（上海交通大学）
University of Technology Sydney（悉尼科技大学）；School of Electrical and Data Engineering（电气与数据工程学院）
The Jaccard index, also known as Intersection-over-Union (IoU score), is one of the most critical evaluation metrics in medical image segmentation. However, directly optimizing the mean IoU (mIoU) score over multiple objective classes is an open problem. Although some algorithms have been proposed to optimize its surrogates, there is no guarantee provided for their generalization ability. In this paper, we present a novel data-distribution-aware margin calibration method for a better generalization of the mIoU over the whole data-distribution, underpinned by a rigid lower bound. This scheme en…
University of Florida（佛罗里达大学）；Herbert Wertheim College of Engineering（赫伯特·韦特海姆工程学院）；McKnight Brain Institute（麦克奈特脑研究所）；College of Public Health and Health Professions（公共卫生与健康职业学院）；College of Medicine（医学院）；United States Air Force Research Laboratory（美国空军研究实验室）
Model calibration measures the agreement between the predicted probability estimates and the true correctness likelihood. Proper model calibration is vital for high-risk applications. Unfortunately, modern deep neural networks are poorly calibrated, compromising trustworthiness and reliability. Medical image segmentation particularly suffers from this due to the natural uncertainty of tissue boundaries. This is exasperated by their loss functions, which favor overconfidence in the majority classes. We address these challenges with DOMINO, a domain-aware model calibration method that leverages …
Interactive 3D Medical Image Segmentation with SAM 2
East China Normal University（华东师范大学）；Tongji University（同济大学）；Shanghai United Imaging Healthcare Advanced Technology Research Institute Co., Ltd.（上海联影医疗科技先进技术研究院有限公司）
本文提出将 3D 医学切片视为视频帧、利用 SAM 2 零样本传播单帧标注的交互式分割流程，实验表明其可在特定场景缩小与监督方法差距并降低标注成本。
Image segmentation is often performed on medical images for identifying diseases in clinical evaluation. Hence it has become one of the major research areas. Conventional image segmentation techniques are unable to provide satisfactory segmentation results for medical images as they contain irregularities. They need to be pre-processed before segmentation. In order to obtain the most suitable method for medical image segmentation, we propose a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker …
Dynamic Prompt Generation for Interactive 3D Medical Image Segmentation Training
Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg（医学物理，诊断与介入放射科，弗赖堡大学医学中心，医学院，弗赖堡大学）；University of Freiburg（弗赖堡大学）；ELLIS Institute Tübingen（图宾根ELLIS研究所）
Nanjing University（南京大学）；University of Wollongong（伍伦贡大学）；Nanjing Normal University（南京师范大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Due to low tissue contrast, irregular object appearance, and unpredictable location variation, segmenting the objects from different medical imaging modalities (e.g., CT, MR) is considered as an important yet challenging task. In this paper, we present a novel method for interactive medical image segmentation with the following merits. (1) Our design is fundamentally different from previous pure patch-based and image-based segmentation methods. We observe that during delineation, the physician repeatedly check the inside-outside intensity changing to determine the boundary, which indicates tha…
ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation
Southern University of Science and Technology（南方科技大学）；University of Hong Kong（香港大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）
Existing automatic 3D image segmentation methods usually fail to meet the clinic use. Many studies have explored an interactive strategy to improve the image segmentation performance by iteratively incorporating user hints. However, the dynamic process for successive interactions is largely ignored. We here propose to model the dynamic process of iterative interactive image segmentation as a Markov decision process (MDP) and solve it with reinforcement learning (RL). Unfortunately, it is intractable to use single-agent RL for voxel-wise prediction due to the large exploration space. To reduce …
The University of British Columbia（不列颠哥伦比亚大学）；Harvard Medical School（哈佛医学院）
Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-the-art results in semantic segmentation for numerous medical imaging applications. Moreover, batch normalization and Dice loss have been used successfully to stabilize and accelerate training. However, these networks are poorly calibrated i.e. they tend to produce overconfident predictions both in correct and erroneous classifications, making them unreliable and hard to interpret. In this paper, we study predictive uncertainty estimation in FCNs for medical image segmentation. We make the following co…
Lightweight Method for Interactive 3D Medical Image Segmentation with Multi-Round Result Fusion
Aerospace Center Hospital（航天中心医院）；Yizhun Medical AI Co., Ltd（医准智能医疗科技有限公司）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Southern University of Science and Technology（南方科技大学）；Beijing Institute of Technology（北京理工大学）
Understanding From Human Perspective: A Multi-agent System for Interactive Egocentric Medical Image Segmentation
School of Instrument Science and Engineering, Southeast University（东南大学仪器科学与工程学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Sichuan University-Pittsburgh Institute, Sichuan University（四川大学匹兹堡学院）；Department of Biomedical Engineering, Case Western Reserve University（凯斯西储大学生物医学工程系）
University College London（伦敦大学学院）；KU Leuven（鲁汶大学）；University Hospitals KU Leuven（鲁汶大学医院）
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes. To address these problems, we propose a novel deep learning-based framework for interactive segmentation by incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose image-specific fine-tuning to make a CNN m…
MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning
Chinese University of Hong Kong, Hong Kong SAR, China（香港中文大学）；Hunyuan Group, Tencent（腾讯洪音集团）；Institute of Automation, the Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所）；Dalian University of Technology, Dalian, China（大连理工大学）；Stanford University, Stanford, USA（斯坦福大学）
ETH Zurich（苏黎世联邦理工学院）；Beijing Institute of Technology（北京理工大学）
Despite recent progress of automatic medical image segmentation techniques, fully automatic results usually fail to meet the clinical use and typically require further refinement. In this work, we propose a quality-aware memory network for interactive segmentation of 3D medical images. Provided by user guidance on an arbitrary slice, an interaction network is firstly employed to obtain an initial 2D segmentation. The quality-aware memory network subsequently propagates the initial segmentation estimation bidirectionally over the entire volume. Subsequent refinement based on additional user gui…
EDUE: Expert Disagreement-Guided One-Pass Uncertainty Estimation for Medical Image Segmentation
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Applied Sciences Western Switzerland (HES-SO)（瑞士西部应用科学大学（HES-SO））
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
We propose iSegFormer, a memory-efficient transformer that combines a Swin transformer with a lightweight multilayer perceptron (MLP) decoder. With the efficient Swin transformer blocks for hierarchical self-attention and the simple MLP decoder for aggregating both local and global attention, iSegFormer learns powerful representations while achieving high computational efficiencies. Specifically, we apply iSegFormer to interactive 3D medical image segmentation.
Efficient Universal Models for Medical Image Segmentation via Weakly Supervised In-Context Learning
Harbin Institute of Technology at Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）；University Hospital Tübingen（图宾根大学医院）；German Center for Mental Health（德国心理健康中心）
Medical image segmentation, the task of partitioning an image into meaningful parts, is an important step toward automating medical image analysis and is at the crux of a variety of medical imaging applications, such as computer aided diagnosis, therapy planning and delivery, and computer aided interventions. However, the existence of noise, low contrast and objects' complexity in medical images are critical obstacles that stand in the way of achieving an ideal segmentation system. Incorporating prior knowledge into image segmentation algorithms has proven useful for obtaining more accurate an…
Xiamen University（厦门大学）；University College London（伦敦大学学院）
Medical image segmentation under federated learning (FL) is a promising direction by allowing multiple clinical sites to collaboratively learn a global model without centralizing datasets. However, using a single model to adapt to various data distributions from different sites is extremely challenging. Personalized FL tackles this issue by only utilizing partial model parameters shared from global server, while keeping the rest to adapt to its own data distribution in the local training of each site. However, most existing methods concentrate on the partial parameter splitting, while do not c…
Image segmentation is a fundamental problem in computational vision and medical imaging. Designing a generic, automated method that works for various objects and imaging modalities is a formidable task. Instead of proposing a new specific segmentation algorithm, we present a general design principle on how to integrate user interactions from the perspective of feedback control theory. Impulsive control and Lyapunov stability analysis are employed to design and analyze an interactive segmentation system. Then stabilization conditions are derived to guide algorithm design. Finally, the effective…
Leipzig University（莱比锡大学）；IfADo - Leibniz Research Centre for Working Environment and Human Factors（莱布尼茨工作环境与人因研究中心）；INRIA Paris（法国国家信息与自动化研究所巴黎中心）；Sorbonne Université（索邦大学）
We present a novel approach that combines machine learning based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large data sets which enables a guided reuse of classifiers. Our approach solves the problem of significant color variability prevalent and often unavoidable in biological and medical images which typically leads to deteriorated segmentation and quantification accuracy thereby greatly reducing the necessary training effort. This increase in efficiency facilitates the quantification of much larg…
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）
In many scenarios where cameras are applied, such as robot positioning and unmanned driving, camera calibration is one of the most important pre-work. The interactive calibration method based on the plane board is becoming popular in camera calibration field due to its repeatability and operation advantages. However, the existing methods select suggestions from a fixed dataset of pre-defined poses based on subjective experience, which leads to a certain degree of one-sidedness. Moreover, they does not give users clear instructions on how to place the board in the specified pose.
We present a meta-learning framework for interactive medical image registration. Our proposed framework comprises three components: a learning-based medical image registration algorithm, a form of user interaction that refines registration at inference, and a meta-learning protocol that learns a rapidly adaptable network initialization. This paper describes a specific algorithm that implements the registration, interaction and meta-learning protocol for our exemplar clinical application: registration of magnetic resonance (MR) imaging to interactively acquired, sparsely-sampled transrectal ult…
The choice of poses for camera calibration with planar patterns is only rarely considered - yet the calibration precision heavily depends on it. This work presents a pose selection method that finds a compact and robust set of calibration poses and is suitable for interactive calibration. Consequently, singular poses that would lead to an unreliable solution are avoided explicitly, while poses reducing the uncertainty of the calibration are favoured. For this, we use uncertainty propagation. Our method takes advantage of a self-identifying calibration pattern to track the camera pose in real-t…
Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation
Johns Hopkins University, School of Medicine（约翰霍普金斯大学医学院）；Massachusetts Institute of Technology（麻省理工学院）；Microsoft Healthcare & Life Sciences（微软医疗健康与生命科学）
Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain. However, these works have primarily focused on classification accuracy, ignoring the important role of uncertainty quantification. Empirically, neural networks are often miscalibrated and overconfident in their predictions. This miscalibration could be problematic in any automatic decision-making system, but we focus on the medical field in which neural network miscalibration has the potential to lead to significant treatment errors.…
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所IIS）；Nuremberg Institute of Technology Georg Simon Ohm（纽伦堡乔治·西蒙·欧姆技术学院）
Would you trust physicians if they cannot explain their decisions to you? Medical diagnostics using machine learning gained enormously in importance within the last decade. However, without further enhancements many state-of-the-art machine learning methods are not suitable for medical application. The most important reasons are insufficient data set quality and the black-box behavior of machine learning algorithms such as Deep Learning models. Consequently, end-users cannot correct the model's decisions and the corresponding explanations. The latter is crucial for the trustworthiness of machi…
MVC-Bench: Benchmarking Calibration of Medical Vision-Language Models
Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Sabaragamuwa University of Sri Lanka（斯里兰卡萨巴拉加穆瓦大学）；Digital Platform Development, SLT PLC（SLT公共有限公司数字平台开发部）
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；Indian Institute of Technology, Delhi（德里印度理工学院）
Semantic segmentation of medical images is an essential first step in computer-aided diagnosis systems for many applications. However, given many disparate imaging modalities and inherent variations in the patient data, it is difficult to consistently achieve high accuracy using modern deep neural networks (DNNs). This has led researchers to propose interactive image segmentation techniques where a medical expert can interactively correct the output of a DNN to the desired accuracy. However, these techniques often need separate training data with the associated human interactions, and do not g…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Siemens Healthineers（西门子医疗）
Although deep learning prediction models have been successful in the discrimination of different classes, they can often suffer from poor calibration across challenging domains including healthcare. Moreover, the long-tail distribution poses great challenges in deep learning classification problems including clinical disease prediction. There are approaches proposed recently to calibrate deep prediction in computer vision, but there are no studies found to demonstrate how the representative models work in different challenging contexts. In this paper, we bridge the confidence calibration from …
Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation
Tryolabs（Tryolabs公司）；Research institute for signals, systems and computational intelligence, sinc(i), FICH-UNL / CONICET（信号、系统与计算智能研究所（sinc(i)，国立拉普拉塔大学计算机科学学院/阿根廷国家科学与技术研究理事会））；LIVIA, ETS Montreal（蒙特利尔高等技术学院LIVIA实验室）；International Laboratory on Learning Systems (ILLS)（国际学习系统实验室（ILLS））
National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）；Stanford University（斯坦福大学）
In medical image classification tasks, it is common to find that the number of normal samples far exceeds the number of abnormal samples. In such class-imbalanced situations, reliable training of deep neural networks continues to be a major challenge. Under these circumstances, the predicted class probabilities may be biased toward the majority class. Calibration has been suggested to alleviate some of these effects. However, there is insufficient analysis explaining when and whether calibrating a model would be beneficial in improving performance. In this study, we perform a systematic analys…
Interactive Medical-SAM2 GUI: A Napari-based semi-automatic annotation tool for medical images
Department of Biomechatronic Engineering, Sungkyunkwan University（生物机电工程系，全州大学）；Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine（神经外科，首尔国立大学医院，首尔国立大学医学院）
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）
The goal of this paper is to interactively refine the automatic segmentation on challenging structures that fall behind human performance, either due to the scarcity of available annotations or the difficulty nature of the problem itself, for example, on segmenting cancer or small organs. Specifically, we propose a novel Transformer-based architecture for Interactive Segmentation (TIS), that treats the refinement task as a procedure for grouping pixels with similar features to those clicks given by the end users. Our proposed architecture is composed of Transformer Decoder variants, which natu…
