Slice interpolation is a fast growing field in medical image processing. Intensity-based interpolation and object-based interpolation are two major groups of methods in the literature. In this paper, we describe an object-oriented, optimization method based on a modified version of curvature-based image registration, in which a displacement field is computed for the missing slice between two known slices and used to interpolate the intensities of the missing slice. The proposed approach is evaluated quantitatively by using the Mean Squared Difference (MSD) as a metric. The produced results also show visual improvement in preserving sharp edges in images.
South China University of Technology（华南理工大学）；Rochester Institute of Technology（罗切斯特理工学院）
We introduce a novel frame-interpolation-based method for slice imputation to improve segmentation accuracy for anisotropic 3D medical images, in which the number of slices and their corresponding segmentation labels can be increased between two consecutive slices in anisotropic 3D medical volumes. Unlike previous inter-slice imputation methods, which only focus on the smoothness in the axial direction, this study aims to improve the smoothness of the interpolated 3D medical volumes in all three directions: axial, sagittal, and coronal. The proposed multitask inter-slice imputation method, in …
GBM Volumetry using the 3D Slicer Medical Image Computing Platform
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；University Hospital of Marburg（马尔堡大学医院）；Isomics, Inc.（Isomics公司）；GE Research（通用电气研究）
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）；Amsterdam Cardiovascular Sciences（阿姆斯特丹心血管科学研究所）；Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学研究所）
High-resolution medical images are beneficial for analysis but their acquisition may not always be feasible. Alternatively, high-resolution images can be created from low-resolution acquisitions using conventional upsampling methods, but such methods cannot exploit high-level contextual information contained in the images. Recently, better performing deep-learning based super-resolution methods have been introduced. However, these methods are limited by their supervised character, i.e. they require high-resolution examples for training. Instead, we propose an unsupervised deep learning semanti…
University of Science and Technology Beijing（北京科技大学）；University of Central Florida（中佛罗里达大学）
For 3D medical image (e.g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly. Previous research on volumetric medical image segmentation in a slice-by-slice manner conventionally use the identical 2D deep neural network to segment all the slices of the same case, ignoring the data heterogeneity among image slices. In this paper, we focus on multi-modal 3D MRI brain tumor segmentation and propose a dynamic architecture network named Med-DANet based on adaptive model selection to achieve effective accuracy and efficiency trade-off. For each slice…
Read Like a Radiologist: Efficient Vision-Language Model for 3D Medical Imaging Interpretation
Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST)（金在哲人工智能研究生院，韩国科学技术院（KAIST））；Yonsei University College of Medicine（延世大学医学院）；Seoul National University College of Medicine（首尔大学医学院）；Seoul National University Hospital（首尔大学医院）；Chung-Ang University College of Medicine（中央大学医学院）；Chungnam National University College of Medicine（忠南大学医学院）
Shanghai Jiao Tong University（上海交通大学）；University of Sydney（悉尼大学）
Dynamic medical imaging is usually limited in application due to the large radiation doses and longer image scanning and reconstruction times. Existing methods attempt to reduce the dynamic sequence by interpolating the volumes between the acquired image volumes. However, these methods are limited to either 2D images and/or are unable to support large variations in the motion between the image volume sequences. In this paper, we present a spatiotemporal volumetric interpolation network (SVIN) designed for 4D dynamic medical images. SVIN introduces dual networks: first is the spatiotemporal mot…
Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices
Vingroup Big Data Institute（Vingroup大数据研究院）；VinBigData JSC（VinBigData股份公司）；VinUni-Illinois Smart Health Center, College of Engineering & Computer Science（VinUni-伊利诺伊智慧健康中心，工程与计算机科学学院）
In-context learning for medical image segmentation
Tohoku University Graduate School of Medicine（东北大学大学院医学系研究科）；Tohoku University Hospital（东北大学医院）；St. Marianna University Graduate School of Medicine（圣玛丽安娜医科大学大学院医学系研究科）
Phase-Aligned Finite-Fourier Periodic Deformation for 4D Medical Image Interpolation
Southern University of Science and Technology（南方科技大学）；Shenzhen University of Advanced Technology（深圳理工大学）；Shenzhen University Medical School（深圳大学医学院）；Shenzhen University（深圳大学）；Beihang University（北京航空航天大学）
Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model
Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；College of AI, Tsinghua University（清华大学人工智能学院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
University Hospital Aachen（亚琛大学医院）；Technical University Dresden（德累斯顿工业大学）；University Hospital Dresden（德累斯顿大学医院）；University Hospital Heidelberg（海德堡大学医院）
Shanghai Jiao Tong University（上海交通大学）；Dianei Technology（点内科技）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（人工智能教育部重点实验室，人工智能研究院，上海交通大学）；Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属上海胸科医院）；Huawei Hisilicon（华为海思）
This paper addresses a fundamental challenge in 3D medical image processing: how to deal with imaging thickness. For anisotropic medical volumes, there is a significant performance gap between thin-slice (mostly 1mm) and thick-slice (mostly 5mm) volumes. Prior arts tend to use 3D approaches for the thin-slice and 2D approaches for the thick-slice, respectively. We aim at a unified approach for both thin- and thick-slice medical volumes. Inspired by recent advances in video analysis, we propose AlignShift, a novel parameter-free operator to convert theoretically any 2D pretrained network into t…
Medical University of Graz（格拉茨医科大学）；Computer Algorithms for Medicine Laboratory（医学计算机算法实验室）；Graz University of Technology（格拉茨工业大学）
Image-based algorithmic software segmentation is an increasingly important topic in many medical fields. Algorithmic segmentation is used for medical three-dimensional visualization, diagnosis or treatment support, especially in complex medical cases. However, accessible medical databases are limited, and valid medical ground truth databases for the evaluation of algorithms are rare and usually comprise only a few images. Inaccuracy or invalidity of medical ground truth data and image-based artefacts also limit the creation of such databases, which is especially relevant for CT data sets of th…
Low-Rank Velocity Fields as a Structural Prior for Unsupervised 4D Medical Image Interpolation
Southern University of Science and Technology（南方科技大学）；Shenzhen University of Advanced Technology（深圳先进技术研究院）；Shenzhen University Medical School（深圳大学医学院）；Shenzhen University（深圳大学）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）
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）
FB-Diff: Fourier Basis-guided Diffusion for Temporal Interpolation of 4D Medical Imaging
Computer Aided Medical Procedures, Technical University of Munich（技术大学慕尼黑计算机辅助医学程序）；Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医学机器人研究所）；Munich Center for Machine Learning, Munich（慕尼黑机器学习中心）；School of Computing, Macquarie University（麦考瑞大学计算机学院）
MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows
Technical University of Munich（慕尼黑技术大学）；TUM University Hospital（TUM大学医院）；LMU Munich（慕尼黑大学）；Imperial College London（伦敦帝国理工学院）；University of Oxford（牛津大学）；Carnegie Mellon University（卡内基梅隆大学）；NVIDIA（NVIDIA公司）；National University of Singapore（新加坡国立大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks. In this paper, we introduce a novel regularization strategy involving interpolation-based mixing for semi-supervised medical image segmentation. The proposed method is a new consistency regularization strategy that encourages segmentation of interpolation of two unlabelled data to be consistent with the interpolation of segmentation maps of those data. This method represents a specific type of data-adaptive regularization paradigm which aids to minimize the overfitting of labelled data under high…
MIT（麻省理工学院）；Massachusetts General Hospital（麻省总医院）；HMS（哈佛医学院）；Google（谷歌）；Cornell University（康奈尔大学）
We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large data sets of clinical images contain a wealth of information, time constraints during acquisition result in sparse scans that fail to capture much of the anatomy. These characteristics often render computational analysis impractical as many image analysis algorithms tend to fail when applied to such images. Highly specialized algorithms that explicitly handle sparse slice spacing do not generalize well across problem…
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…
Given the prevalence of 3D medical imaging technologies such as MRI and CT that are widely used in diagnosing and treating diverse diseases, 3D segmentation is one of the fundamental tasks of medical image analysis. Recently, Transformer-based models have started to achieve state-of-the-art performances across many vision tasks, through pre-training on large-scale natural image benchmark datasets. While works on medical image analysis have also begun to explore Transformer-based models, there is currently no optimal strategy to effectively leverage pre-trained Transformers, primarily due to th…
Despite the surge of deep learning in the past decade, some users are skeptical to deploy these models in practice due to their black-box nature. Specifically, in the medical space where there are severe potential repercussions, we need to develop methods to gain confidence in the models' decisions. To this end, we propose a novel medical imaging generative adversarial framework, medXGAN (medical eXplanation GAN), to visually explain what a medical classifier focuses on in its binary predictions. By encoding domain knowledge of medical images, we are able to disentangle anatomical structure an…
University of Padova（帕多瓦大学）；Padua University Hospital（帕多瓦大学医院）；University of Genova（热那亚大学）
To analyse multimodal 3-dimensional medical images, interpolation is required for resampling which - unavoidably - introduces an interpolation error. In this work we consider three segmented 3-dimensional images resampled with three different neuroimaging software tools for comparing undersampling and oversampling strategies and to identify where the oversampling error lies. The results indicate that undersampling to the lowest image size is advantageous in terms of mean value per segment errors and that the oversampling error is larger where the gradient is steeper, showing a Gibbs effect.
National Tsing Hua University（国立清华大学）；Chang Gung Memorial Hospital（长庚纪念医院）；National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）
Tomography medical imaging is essential in the clinical workflow of modern cancer radiotherapy. Radiation oncologists identify cancerous tissues, applying delineation on treatment regions throughout all image slices. This kind of task is often formulated as a volumetric segmentation task by means of 3D convolutional networks with considerable computational cost. Instead, inspired by the treating methodology of considering meaningful information across slices, we used Gated Graph Neural Network to frame this problem more efficiently. More specifically, we propose convolutional recurrent Gated G…
Brigham and Women’s Hospital, Harvard Medical School（哈佛医学院布莱根妇女医院）；University Hospital of Marburg（马尔堡大学医院）；The Philipps-University of Marburg（马尔堡菲利普斯大学）
ToNNO: Tomographic Reconstruction of a Neural Network's Output for Weakly Supervised Segmentation of 3D Medical Images
TheraPanacea；University of Crete（克里特大学）；IACM, FORTH（FORTH 应用与计算数学研究所）；Archimedes, Athena RC（雅典研究中心阿基米德研究所）；CentraleSupélec, University of Paris-Saclay（中央理工-高等电力学院，巴黎-萨克雷大学）
MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（科学技术大学生物医学工程学院）；Suzhou Institute for Advanced Research, University of Science and Technology of China（科学技术大学苏州市先进研究所）；Center for Medical Imaging, Robotics, and Analytic Computing & LEarning (MIRACLE), Suzhou Institute for Advanced Research, USTC（医学影像、机器人与分析计算与学习中心）；Department of Automation, Shanghai Jiao Tong University（上海交通大学自动化系）；Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University（上海交通大学图像处理与模式识别研究所）；Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医学机器人研究所）；State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China（科学技术大学精密与智能化学国家重点实验室）
