Automated and accurate 3D medical image segmentation plays an essential role in assisting medical professionals to evaluate disease progresses and make fast therapeutic schedules. Although deep convolutional neural networks (DCNNs) have widely applied to this task, the accuracy of these models still need to be further improved mainly due to their limited ability to 3D context perception. In this paper, we propose the 3D context residual network (ConResNet) for the accurate segmentation of 3D medical images. This model consists of an encoder, a segmentation decoder, and a context residual decoder. We design the context residual module and use it to bridge both decoders at each scale. Each context residual module contains both context residual mapping and context attention mapping, the formal aims to explicitly learn the inter-slice context information and the latter uses such context as a kind of attention to boost the segmentation accuracy. We evaluated this model on the MICCAI 2018 Brain Tumor Segmentation (BraTS) dataset and NIH Pancreas Segmentation (Pancreas-CT) dataset. Our results not only demonstrate the effectiveness of the proposed 3D context residual learning scheme but also indicate that the proposed ConResNet is more accurate than six top-ranking methods in brain tumor segmentation and seven top-ranking methods in pancreas segmentation. Code is available at https://git.io/ConResNet
South China University of Technology（华南理工大学）；University of Utah（犹他大学）
We introduce the idea of inter-slice image augmentation whereby the numbers of the medical images and the corresponding segmentation labels are increased between two consecutive images in order to boost medical image segmentation accuracy. Unlike conventional data augmentation methods in medical imaging, which only increase the number of training samples directly by adding new virtual samples using simple parameterized transformations such as rotation, flipping, scaling, etc., we aim to augment data based on the relationship between two consecutive images, which increases not only the number b…
MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；Department of Vascular Surgery, Peking Union Medical College Hospital（血管外科部门，北京协和医科大学医院）
Southeast University（东南大学）；Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing（江苏省医学信息处理联合国际研究实验室）；Centre de Recherche en Information Biomédicale Sino-Français (CRIBs)（中法生物医学信息研究中心（CRIBs））
The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent sparse inter-slice information and dense intra-slice information in a balanced way, leading to severe underfitting to inter-slice features (for vanilla 2D CNNs) and overfitting to noise from long-range slices (for vanilla 3D CNNs). In this work, a novel mesh network (MNet) is proposed to balance the spatial representation inter axes via learning. 1) Our MNet latently fuses plenty of representation processes by embedding …
Xidian University（西安电子科技大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
3D image segmentation is one of the most important and ubiquitous problems in medical image processing. It provides detailed quantitative analysis for accurate disease diagnosis, abnormal detection, and classification. Currently deep learning algorithms are widely used in medical image segmentation, most algorithms trained models with full annotated datasets. However, obtaining medical image datasets is very difficult and expensive, and full annotation of 3D medical image is a monotonous and time-consuming work. Partially labelling informative slices in 3D images will be a great relief of manu…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Biomedical Engineering, University of Science and Technology of China（中国科学技术大学生物医学工程学院）
针对 SAM 难以建模三维医学切片上下文且全体积提示成本高的问题，本文提出 Slide-SAM，利用三切片滑动窗口和相邻掩码传播提示，实现极少提示下的高效三维分割。
Umeå University（于默奥大学）；Eindhoven University of Technology（埃因霍温理工大学）
When using Convolutional Neural Networks (CNNs) for segmentation of organs and lesions in medical images, the conventional approach is to work with inputs and outputs either as single slice (2D) or whole volumes (3D). One common alternative, in this study denoted as pseudo-3D, is to use a stack of adjacent slices as input and produce a prediction for at least the central slice. This approach gives the network the possibility to capture 3D spatial information, with only a minor additional computational cost. In this study, we systematically evaluate the segmentation performance and computationa…
VoxShield: Protecting 3D Medical Datasets from Unauthorized Training via Frequency-Aware Inter-Slice Disruption
Westlake University（西拉雅大学）；Dalian University of Technology（大连理工大学）；Hokkaido University（北海道大学）；The Chinese University of Hong Kong（香港中文大学）；RIKEN（理化学研究所）
Segmentation is a critical step in medical image analysis. Fully Convolutional Networks (FCNs) have emerged as powerful segmentation models achieving state-of-the-art results in various medical image datasets. Network architectures are usually designed manually for a specific segmentation task so applying them to other medical datasets requires extensive experience and time. Moreover, the segmentation requires handling large volumetric data that results in big and complex architectures. Recently, methods that automatically design neural networks for medical image segmentation have been present…
Recent advances in transformer-based models have drawn attention to exploring these techniques in medical image segmentation, especially in conjunction with the U-Net model (or its variants), which has shown great success in medical image segmentation, under both 2D and 3D settings. Current 2D based methods either directly replace convolutional layers with pure transformers or consider a transformer as an additional intermediate encoder between the encoder and decoder of U-Net. However, these approaches only consider the attention encoding within one single slice and do not utilize the axial-a…
Towards Voxel Spacing Consistency for Medical Image Segmentation
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究院）；Department of Ultrasound, Shanghai Chest Hospital（上海市胸科医院超声科）；CAMPAR, Technical University of Munich（慕尼黑工业大学CAMPAR）
Recent trends in semi-supervised learning have significantly boosted the performance of 3D semi-supervised medical image segmentation. Compared with 2D images, 3D medical volumes involve information from different directions, e.g., transverse, sagittal, and coronal planes, so as to naturally provide complementary views. These complementary views and the intrinsic similarity among adjacent 3D slices inspire us to develop a novel annotation way and its corresponding semi-supervised model for effective segmentation. Specifically, we firstly propose the orthogonal annotation by only labeling two o…
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…
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。
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation
RWTH Aachen University（亚琛工业大学）；Iran University of Science and Technology（伊朗科技大学）；Shahid Beheshti University（沙希德·贝赫什提大学）；Northwestern University（西北大学）；University of Regensburg（雷根斯堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）
Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）
Technical University of Munich（慕尼黑工业大学）；Ulm University（乌尔姆大学）
The lack of sufficient annotated image data is a common issue in medical image segmentation. For some organs and densities, the annotation may be scarce, leading to poor model training convergence, while other organs have plenty of annotated data. In this work, we present MetaMedSeg, a gradient-based meta-learning algorithm that redefines the meta-learning task for the volumetric medical data with the goal to capture the variety between the slices. We also explore different weighting schemes for gradients aggregation, arguing that different tasks might have different complexity, and hence, con…
Segmentation of organs of interest in 3D medical images is necessary for accurate diagnosis and longitudinal studies. Though recent advances using deep learning have shown success for many segmentation tasks, large datasets are required for high performance and the annotation process is both time consuming and labor intensive. In this paper, we propose a 3D few shot segmentation framework for accurate organ segmentation using limited training samples of the target organ annotation. To achieve this, a U-Net like network is designed to predict segmentation by learning the relationship between 2D…
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局资讯通信研究院）；National University of Singapore（新加坡国立大学）；Singapore General Hospital（新加坡中央医院）；Duke-NUS Medical School Singapore（新加坡杜克-新加坡国立大学医学院）
Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring. Recently, deep convolutional neural network (DCNNs) has obtained tremendous success in 2D and 3D medical image segmentation. However, 2D DCNNs cannot fully leverage the inter-slice information, while 3D DCNNs are computationally expensive and memory intensive. To address these issues, we first propose a novel dense-sparse training flow from a data perspective, in which, densely adjacent slices and sparsely adjacent slices are extracted as inputs for regularizing DCNNs, thereby impro…
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 …
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset
University of British Columbia（不列颠哥伦比亚大学）；BC Cancer Research Institute（不列颠哥伦比亚癌症研究所）；Microsoft（微软公司）；Seoul St. Mary’s Hospital（首尔圣玛丽医院）；The Catholic University of Korea（韩国天主教大学）；BC Cancer（不列颠哥伦比亚癌症中心）
Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学现代软件工程国家重点实验室）；National Institute of Healthcare Data Science, Nanjing University（南京大学医疗数据科学国家研究院）
SliceMamba with Neural Architecture Search for Medical Image Segmentation
School of Computer Science, Beijing University of Technology（北京工业大学计算机科学学院）；Xiaomi Inc（小米公司）；Department of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital（首都医科大学附属北京友谊医院放射科）；New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别新实验室）
Xidian University（西安电子科技大学）；Northwestern Polytechnical University（西北工业大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；Nankai University（南开大学）；City University of Hong Kong（香港城市大学）
Due to the constraints of the imaging device and high cost in operation time, computer tomography (CT) scans are usually acquired with low intra-slice resolution. Improving the intra-slice resolution is beneficial to the disease diagnosis for both human experts and computer-aided systems. To this end, this paper builds a novel medical slice synthesis to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy to accomplish this task in the sel…
Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；Radboud University Medical Center（拉德堡德大学医学中心）；Jacobs University（雅各布斯大学）；University of Bremen（不来梅大学）
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…
The development of high quality medical image segmentation algorithms depends on the availability of large datasets with pixel-level labels. The challenges of collecting such datasets, especially in case of 3D volumes, motivate to develop approaches that can learn from other types of labels that are cheap to obtain, e.g. bounding boxes. We focus on 3D medical images with their corresponding 3D bounding boxes which are considered as series of per-slice non-tight 2D bounding boxes. While current weakly-supervised approaches that use 2D bounding boxes as weak labels can be applied to medical imag…
Deep learning-based single image super-resolution (SISR) methods face various challenges when applied to 3D medical volumetric data (i.e., CT and MR images) due to the high memory cost and anisotropic resolution, which adversely affect their performance. Furthermore, mainstream SISR methods are designed to work over specific upsampling factors, which makes them ineffective in clinical practice. In this paper, we introduce a Spatially Aware Interpolation NeTwork (SAINT) for medical slice synthesis to alleviate the memory constraint that volumetric data poses. Compared to other super-resolution …
Medical image slice interpolation is an active field of research. The methods for this task can be categorized into two broad groups: intensity-based and object-based interpolation methods. While intensity-based methods are generally easier to perform and less computationally expensive, object-based methods are capable of producing more accurate results and account for deformable changes in the objects within the slices. In this paper, performance of two well-known object-based interpolation methods is analyzed and compared. Here, a deformable registration-based method specifically designed fo…
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）
LPIXEL Inc.（LPIXEL公司）；University of Cambridge（剑桥大学）；National Institute of Informatics（国立信息学研究所）；National Center for Global Health and Medicine（国立国际医疗研究中心）；Juntendo University（顺天堂大学）；ELTE Eötvös Loránd University（厄特沃什·罗兰大学）；The University of Tokyo（东京大学）
Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with eith…
