Scale-space segmentation
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TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation Open
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net…
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U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications Open
U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary …
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Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning Open
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 …
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A review of semantic segmentation using deep neural networks Open
During the long history of computer vision, one of the grand challenges has been semantic segmentation which is the ability to segment an unknown image into different parts and objects (e.g., beach, ocean, sun, dog, swimmer). Furthermore, …
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Recurrent residual U-Net for medical image segmentation Open
Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation…
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A Review of Deep-Learning-Based Medical Image Segmentation Methods Open
As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid d…
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A large annotated medical image dataset for the development and evaluation of segmentation algorithms Open
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data …
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Medical image segmentation using deep learning: A survey Open
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation using deep…
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STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation Open
Recently, significant improvement has been made on semantic object segmentation due to the development of deep convolutional neural networks (DCNNs). Training such a DCNN usually relies on a large number of images with pixel-level segmenta…
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ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images Open
Obtaining quantitative measures from biomedical images often requires segmentation, i.e., finding and outlining the structures of interest. Multi-modality imaging datasets, in which multiple imaging measures are available at each spatial l…
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Saliency-Aware Video Object Segmentation Open
Video saliency, aiming for estimation of a single dominant object in a sequence, offers strong object-level cues for unsupervised video object segmentation. In this paper, we present a geodesic distance based technique that provides reliab…
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NAS-Unet: Neural Architecture Search for Medical Image Segmentation Open
Neural architecture search (NAS) has significant progress in improving the accuracy of image classification. Recently, some works attempt to extend NAS to image segmentation which shows preliminary feasibility. However, all of them focus o…
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DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation Open
Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic r…
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Deep Extreme Cut: From Extreme Points to Object Segmentation Open
© 2018 IEEE. This paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos. We do so by adding an extra channel to the image in t…
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Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation Open
Deep learning (DL) based semantic segmentation methods have been providing state-of-the-art performance in the last few years. More specifically, these techniques have been successfully applied to medical image classification, segmentation…
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Image Segmentation Using Text and Image Prompts Open
Image segmentation is usually addressed by training a model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive as it requires re-training the model on a dataset that encompasses t…
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Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation Open
A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely used for grayscale and color image segmentation. However, most of them are time-consuming and unable to provide desired segmentation results for color im…
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Lung CT Image Segmentation Using Deep Neural Networks Open
Lung CT image segmentation is a necessary initial step for lung image analysis, it is a prerequisite step to provide an accurate lung CT image analysis such as lung cancer detection. In this work, we propose a lung CT image segmentation us…
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Automatic Instrument Segmentation in Robot-Assisted Surgery using Deep Learning Open
Semantic segmentation of robotic instruments is an important problem for the\nrobot-assisted surgery. One of the main challenges is to correctly detect an\ninstrument's position for the tracking and pose estimation in the vicinity of\nsurg…
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Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images Open
The author proposes a method for simultaneous registration and segmentation of multi-source images, using the multivariate mixture model (MvMM) and maximum of log-likelihood (LL) framework. Specifically, the method is applied to the proble…
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U-Net-Based Medical Image Segmentation Open
Deep learning has been extensively applied to segmentation in medical imaging. U-Net proposed in 2015 shows the advantages of accurate segmentation of small targets and its scalable network architecture. With the increasing requirements fo…
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YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark Open
Learning long-term spatial-temporal features are critical for many video analysis tasks. However, existing video segmentation methods predominantly rely on static image segmentation techniques, and methods capturing temporal dependency for…
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A Review of Deep Learning-Based Semantic Segmentation for Point Cloud Open
In recent years, the popularity of depth sensors and 3D scanners has led to a rapid development of 3D point clouds. Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention of resear…
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Techniques and Challenges of Image Segmentation: A Review Open
Image segmentation, which has become a research hotspot in the field of image processing and computer vision, refers to the process of dividing an image into meaningful and non-overlapping regions, and it is an essential step in natural sc…
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Image segmentation evaluation: a survey of methods Open
Image segmentation is a prerequisite for image processing. There are many methods for image segmentation, and as a result, a great number of methods for evaluating segmentation results have also been proposed. How to effectively evaluate t…
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The 2018 DAVIS Challenge on Video Object Segmentation Open
We present the 2018 DAVIS Challenge on Video Object Segmentation, a public competition specifically designed for the task of video object segmentation. It builds upon the DAVIS 2017 dataset, which was presented in the previous edition of t…
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Deep Neural Networks for Medical Image Segmentation Open
Image segmentation is a branch of digital image processing which has numerous applications in the field of analysis of images, augmented reality, machine vision, and many more. The field of medical image analysis is growing and the segment…
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Semantic Instance Segmentation for Autonomous Driving Open
© 2017 IEEE. Semantic instance segmentation remains a challenge. We propose to tackle the problem with a discriminative loss function, operating at pixel level, that encourages a convolutional network to produce a representation of the ima…
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LViT: Language Meets Vision Transformer in Medical Image Segmentation Open
Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data …
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Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation Open
The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies and individual expe…