Medical image semantic segmentation The recent developments in computer vision algorithms and the increasing availability of Dec 17, 2024 · Recently, developing unified medical image segmentation models gains increasing attention, especially with the advent of the Segment Anything Model (SAM). Although the symmetrical structure of the U-Net model enables this network to encode rich May 1, 2023 · (a) Cell samples from the GlaS dataset (b) SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation (c) ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data (d) Recurrent residual U-Net for medical image segmentation (e) Attention U-Net: Learning Where to Look for the Pancreas (f) U-Net Dec 3, 2024 · Medical imaging is essential in healthcare to provide key insights into patient anatomy and pathology, aiding in diagnosis and treatment. Introduction Medical image segmentation aims to segment regions of in-terest in medical images, playing a vital role for medical analysis and clinical diagnosis [10,15,27,44 Dec 12, 2024 · Semi-supervised learning has attracted more and more attention in medical image segmentation as it alleviates reliance on high-cost annotated data. , Cross-Entropy Loss, Mean Square Loss) to handle the unbalanced label Medical image segmentation plays a critical role in clinical processes and medical research, such as disease diagnosis (Zhu et al. Before 2021, the majority of networks utilized the U-Net structure as a basis for improvement in medical image segmentation, fundamentally leveraging CNNs to extract inductive bias information from images. Jul 23, 2023 · Image segmentation plays an essential role in medical image analysis as it provides automated delineation of specific anatomical structures of interest and further enables many downstream tasks such as shape analysis and volume measurement. First, we collect and standardize over 6 Jun 9, 2023 · Interest in medical image segmentation has grown in the last decade due to advances in machine learning. Based on exploratory experiments, features at multiple scales have been found to be of great importance for the segmentation of medical Feb 19, 2024 · The dynamical system perspective has been used to build efficient image classification networks and semantic segmentation networks. cross-entropy) and region-wise (e. However, 1) there exists few works exploiting DOI: 10. We sought to create a large collection of annotated medical image datasets of various clinically relevant Image segmentation is an integral component of medical image analysis, and precise and stable image segmentation algorithms can provide important insights into the comprehensive analysis of anatomical regions, which is of paramount importance for lesions visualization, accurate diagnosis of diseases and the formulation of treatment plans in Sep 15, 2022 · 3. Application of UNET in medical image segmentation. 2021a). Deep learning models, like the Segment Anything Model (SAM), have been proposed as a powerful tool that helps to delimit regions using a prompt. inserted an attention gate into the skip connection of U-Net. Nov 17, 2022 · In this article, we look into some essential aspects of convolutional neural networks (CNNs) with the focus on medical image segmentation. While deep learning has excelled in automating this task, a major hurdle is the need for numerous annotated segmentation masks, which are resource-intensive to produce due to the required expertise and time. Consequently, segmentation networks are inefficient and less generalizable across different organs. Aug 30, 2024 · Semantic segmentation of medical images is pivotal in applications like disease diagnosis and treatment planning. In clinical practice, medical imaging techniques include 2D video-based examinations that capture sequential scans, and 3D volumetric imaging that forms a comprehensive 3D representation from a stack of 2D slices. 2 SUPERVISED LEARNING. Semantic segmentation is the classification of features in images based on pixels. Dec 20, 2024 · Although semi-supervised learning has made significant advances in the field of medical image segmentation, fully annotating a volumetric sample slice by slice remains a costly and time-consuming task. Apr 23, 2023 · In recent years, deep learning has achieved good results in the semantic segmentation of medical images. Accurate semantic segmentation of medical images is of significant importance for subsequent processing and analysis. To address these Nov 19, 2024 · Implemented in one code library. Among several applications, medical image semantic segmentation is one of the core areas of active research to delineate the anatomical structures and other regions of interest. Nov 11, 2022 · There have been major developments in deep learning in computer vision since the 2010s. , 2000) is an important and active research problem. To address these issues, we proposed a novel framework named nmODE-Unet, which is based on the Nov 19, 2024 · Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model generalization and consistent evaluation across different models. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment Feb 1, 2021 · Based on the great success of DenseNets in medical images segmentation [2], [30], [35], we propose an efficient, 3D-DenseUNet-569, 3D deep learning model for liver and tumor semantic segmentation. In the domain of medical image segmentation, it has steadily evolved into a design paradigm because of its outstanding results in comparison to other kinds of models. Semantic segmentation partitions raw image data into structured and meaningful regions and thus Sep 28, 2020 · 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. The U-Net model has become the standard design choice. Barath Narayanan, University of Dayton Research Institute (UDRI) with co-authors: Dr. Feb 2, 2024 · Semantic segmentation, as one of the computer vision tasks, seeks to assign each pixel in an image to one of several predefined classes, essentially pixel-level image classification. binary medical segmentation and task-specific methods in semantic medical segmentation, showcasing promising re-sults and potential for broader medical applications. However, these latent semantic representations rely heavily on labor-intensive pixel-level annotations Mar 1, 2018 · The image semantic segmentation has been extensively studying. While significant Oct 1, 2023 · Previous TTA methods applied to medical image segmentation tasks usually carry out a global domain adaptation for all semantic categories, but global domain adaptation would be sub-optimal as the influence of domain shift on different semantic categories may be different. In Semseg technique, every pixel of an image is classified into an instance, where each class is corresponded by an instance. On the one hand, there is often a ``soft boundary'' between foreground and background in medical images, with poor illumination and low contrast further reducing the Aug 16, 2024 · Figure 1: (a) Illustration of how human annotators learn to segment medical images: By studying a few annotated examples, the human annotator can effectively apply the learned knowledge to segment a new, unseen case. Early traditional medical image segmentation methods largely focused on edge detection, template matching technology, region growing, graph cutting, and other mathematical methods. 1. This paper makes two original contributions. To provide a gating signal, Oktay et al. It provides fair evaluation and comparison of CNNs and Transformers on multiple medical image datasets Dec 6, 2023 · Semantic Segmentation plays a pivotal role in many applications related to medical image and video analysis. The segmentation mask is obtained by removing the noise through multistep iteration. Apr 25, 2022 · Segmentation in medical imaging deals with labelling each pixel on the image with a class which is known as Semantic segmentation. Introduction Medical image analysis has become indispensable in clinical diagnosis and complementary medicine with the develop-ments in medical imaging technology. 1. Even worse, most of the existing approaches pay much attention to image-level information and ignore semantic features, resulting in the inability to perceive weak boundaries. However, the design of the segmentation networks is fragmented and lacks a mathematical explanation. We present a novel structural tensor loss (STL) to guide feature learning on the spatial domain for semi-supervised semantic segmentation. To Apr 25, 2022 · Recent decades have witnessed rapid development in the field of medical image segmentation. Effective analysis of these images requires precise segmentation to art weakly supervised methods on point-supervised medical image semantic segmentation tasks. , 2019), robotic surgery (Colleoni et al. A medical image segmentation method is proposed based on multi-dimensional statistical features as shown in Figure 1. g. Recently, vision mamba (ViM) models have emerged as promising solutions for addressing model complexities by excelling in long-range feature iterations with linear complexity. Firstly, compared to traditional surveys that directly divide Official pytorch implementation of the paper Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation This work presents a novel deep multi-task learning method for medical image segmentation leveraging Histogram of Oriented Gradients (HOGs) to generate pseudo-labels. Such studies can be roughly categorized into two groups: traditional methods and deep learning methods. Nevertheless, due to the small size of diabetic retinopathy lesions and the high interclass similarity in terms of location, color, and shape among different lesions, the segmentation task is highly challenging. Zhang and X. In this paper, we introduce the IMed-361M benchmark dataset, a significant advancement in general IMIS research. dice) losses while boundary-wise loss is underexplored. , 2022a) also incorporate shape information to alleviate the problem of insufficient labeled images in semi-supervised medical image segmentation. This method integrates CNNs and Transformer into the feature extraction network, and designs a texture statistics extraction module (TSEM) for the extraction and fusion of multi-dimensional Medical image segmentation plays a critical role in computer-aided diagnosis, while the diversity and complexity of medical images make it difficult to segment precisely. Jan 18, 2021 · Background The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Skip connections and multiple scaling are Dec 17, 2024 · Abstract page for arXiv paper 2412. The modern methods rely on the deep convolutional neural networks, which can be trained to address this problem. It is known that one of the key aims of semantic segmentation is to precisely delineate objects' boundaries. Firstly, the data is augmented through translation, rotation, contrast enhancement, and other expansion methods. So, it is important to note that, if one is not using the BraTS data and/or is using different/additional data, it must comply with the BraTS dataset folder structure which will be described in the subsequent points. an advanced semi-supervised multi-class medical image semantic segmentation framework is proposed, evaluated on a public benchmark data set with a variety of evaluation measures, and keeps state-of-the-art against other semi-supervised methods under the same setting and feature information distribution to our best of knowledge [30, 32, 39, 41 May 2, 2024 · Artificial Intelligence (XAI) has found numerous applications in computer vision. Wei Dai [ 36 ] was inspired by the structure of Luc et al. However, designing a neural network architecture for medical image and surgical video segmentation is challenging due to the diverse features of relevant classes, including heterogeneity, deformability, transparency, blunt boundaries, and various distortions. SOTA medical image segmentation methods based on various challenges - JunMa11/SOTA-MedSeg Cascaded Semantic Segmentation for Kidney and Tumor : 0. Already implemented pipelines are commonly standalone software, optimized on a specific public data set Dec 17, 2024 · Robustness and generalizability in medical image segmentation are often hindered by scarcity and limited diversity of training data, which stands in contrast to the variability encountered during inference. In practice, medical images of specific modalities (e. In the medical field, the well-annotated samples are limited due to privacy protection and the requirement of clinical expertise. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. Semantic Segmentation has a plethora of applications in the healthcare industry. Therefore, lesions can be outlined from normal areas in medical datasets. Nov 25, 2024 · Medical Segmentation Decathlon 6 supports creating and benchmarking semantic segmentation algorithms. Hence, the Runge–Kutta segmentation network (RKSeg) for medical image segmentation was born. 12492: DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation Semi-supervised medical image segmentation (SSMIS) uses consistency learning to regularize model training, which alleviates the burden of pixel-wise manual annotations. Segmenting objects in medical images and extracting their features can help clinicians to perform accurate Oct 8, 2024 · Semantic segmentation is mostly used to segment medical images on the basis of different features and its goal is to label each pixel of an image with the corresponding class of the objects. Jan 22, 2024 · Here we present MedSAM, a foundation model designed for bridging this gap by enabling universal medical image segmentation. However, most existing approaches are restricted by the limited receptive field for failing to capture longrange dependencies, meanwhile lacking global features Jul 1, 2023 · Semantic segmentation is the most important branch of medical image analysis, and many clinical applications are established based on it, such as diagnosis (Yang et al. The UNET model [16] designed by Olaf Ronneberger, Philipp Nov 14, 2024 · Medical image segmentation constitutes a crucial step in the analysis of medical images, possessing extensive applications and research significance within the realm of medical research and practice. Oct 28, 2024 · In this paper, we propose a UNet-based multi-scale context fusion algorithm for medical image segmentation, which extracts rich contextual information by extracting semantic information at Jan 1, 2024 · As shown in Fig. This scenario often leads to ultra low-data regimes, where annotated 5 days ago · The precise and automated segmentation of anatomical structures within 3D medical image is essential for many medical practices, such as computer-assisted diagnosis, image-guided interventions, and radiation therapy [1], [2]. A model trained with imbalanced data tends to bias towards healthy data which is not desired in clinical applications and Nov 18, 2024 · The advancement of medical image segmentation techniques has been propelled by the adoption of deep learning techniques, particularly UNet-based approaches, which exploit semantic information to improve the accuracy of segmentations. [ 34 ] and proposed a Structure Correcting Adversarial Network (SCAN) using FCN as a All the scripts (whichever are relavent) are written with repect to the data folder structure of the BraTS dataset. It includes 2633 3D images from ten anatomical sites and modalities collected from multiple art weakly supervised methods on point-supervised medical image semantic segmentation tasks. Tao: Code: Arxiv: 2024-03-12: Large Window-based Mamba UNet for Medical Image Segmentation: Beyond Convolution and Self-attention: J. , 2021). However, obtaining such high-quality Oct 8, 2021 · Medical imaging contributes significantly to progress in scientific discoveries and medicine 1. In this blog, we apply Deep Learning based segmentation to skin lesions in dermoscopic images to aid in melanoma detection. 5 days ago · Recent advances in deep learning have shown that learning robust feature representations is critical for the success of many computer vision tasks, including medical image segmentation. This Sep 16, 2024 · Therefore, we seek to introduce structural and frequency information to improve the performance of semi-supervised semantic segmentation for medical images. Specifically, STL utilizes the structural Jan 8, 2024 · Purpose Semantic segmentation plays a pivotal role in many applications related to medical image and video analysis. Depending solely on visual features hampers the model's capacity to adapt effectively to various domains Jun 8, 2023 · Medical image segmentation is used to extract regions of interest (ROIs) from medical images and videos. This technique is crucial for assisted diagnosis, disease monitoring and treatment planning. Pseudo-labeling focuses on selecting reliable pseudo-labels, while co-training emphasizes sub-network diversity for complementary information extraction. 9674: 0. 2006. However, the effectiveness of these methods Feb 25, 2019 · Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. This technique has become a vital part of image analysis nowadays as it facilitates the description, categorization, and visualization of the regions of interest in an image. For medical image segmentation tasks, supervised learning is the most popular method since these tasks usually require high accuracy. SAM has shown promising binary segmentation performance in natural domains, however, transferring it to the medical domain remains challenging, as medical images often possess substantial inter-category overlaps. In clinical practice, achieving pre-cise segmentation results necessitates manual implementa-tion and there is an urgent need for automatic The semi-supervised medical image segmentation approaches can be broadly classified into two categories. This paper has introduced a new architecture for doing semantic segmentation which is significantly May 18, 2024 · A medical image semantic segmentation framework, namely, TransDiff, is proposed that employs a VAE as the encoder and decoder. Furthermore, the inherent Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc Jul 31, 2021 · Semantic image segmentation is a popular image segmentation technique where each pixel in an image is labeled with an object class. , 2020), and efficacy evaluation (Heller et al. Dec 2, 2024 · 3D medical image segmentation is a key step in numerous clinical applications. Hardie, and Redha Ali. Segmentation of anatomical structures and pathological regions in medical images is essential for modern clinical diagnosis, disease research, and treatment planning. However, in real-world medical scenarios, the Oct 27, 2022 · Medical image segmentation is a fundamental step for medical image analysis and it is also a key component in delineating anatomical structure in medical images. This assistive method facilitates the cancer detection process and provides a benchmark to highlight the affected area. Feb 1, 2023 · Semantic-based segmentation (Semseg) methods play an essential part in medical imaging analysis to improve the diagnostic process. A few years ago networks require the huge dataset to be trained. Wu: Code: Arxiv: 2024-03-08: LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation: W May 16, 2024 · For medical image segmentation, this is the initial end-to-end network. FuseNet leverages the shared semantic dependencies between the original and augmented images to . Semantic image segmentation is a computer vision task where machines or algorithms label specific regions of an As for the optimization of the DNN-based medical image segmentation models, researchers in the medical image segmentation domain tend to use yje Dice Loss or a combination of Dice loss and cross-entropy as a total loss [45] instead of the classical loss functions (e. It has a significant Jan 3, 2025 · Medical image segmentation demands the aggregation of global and local feature representations, posing a challenge for current methodologies in handling both long-range and short-range feature interactions. 1109/SSIAI. In response to this challenge, we introduce FuseNet, a dual-stream framework for self-supervised semantic segmentation that eliminates the need for manual annotation. , 2020), etc Jan 1, 2025 · Considering that objects of interest in medical images usually have specific shapes, some works (Li et al. , 2020), radiotherapy planning and follow-ups (Nemoto et al. Though immensely effective, such networks only take into account localized features and are unable to capitalize on the global context of medical image Dec 6, 2022 · The well-known semantic segmentation technique is used in medical image analysis to identify and label regions of images. Affiliations: *Sensors and Software Systems, University of Dayton Research Institute, 300 College Park, Dayton, OH, 45469 Medical Image Segmentation. To address this, we propose May 1, 2023 · Medical image semantic segmentation aims at delineating pathological and anatomical structures from medical images of various modalities [14], [15], such as Computed Tomography (CT), X-ray, Magnetic Resonance Imaging (MRI), and ultrasound. , segmentation. Supervised learning techniques for medical image segmentation often require a substantial amount of annotated data [5,6,7,8,9]. However, the order of organs in scanned images has been disregarded by current medical image segmentation approaches based on UNet. However, acquiring large labeled datasets remains unattainable due to the substantial expertise and time required Sep 28, 2024 · This paper introduces MedCLIP-SAMv2, a novel framework that integrates the CLIP and SAM models to perform segmentation on clinical scans using text prompts, in both zero-shot and weakly supervised settings. Semantic segmentation network. , 2020, Luo et al. AI, artificial intelligence. The model is developed on a large-scale medical image dataset with Dec 1, 2024 · In this paper, we propose a unified semantic model (UniSEM) to capture sufficient semantic information from both cross image and single image perspectives to enhance the feature representations of pixels. A recent breakthrough, the Hadamard Layer, is a new simple, computationally efficient way to improve results in semantic segmentation tasks. However, the recent advances in deep learning allow training networks on the small datasets, which is a critical issue for medical images, since the Jun 4, 2021 · Deep learning has an enormous impact on medical image analysis. , 2021a, Meng et al. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Aug 21, 2024 · Medical image segmentation based on deep learning. 1633722 Corpus ID: 17858176; Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm @article{Ng2006MedicalIS, title={Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm}, author={Hsiao Piau Ng and Sim Heng Ong and Kelvin Weng Chiong Foong and Poh-Sun Goh and Wieslaw Lucjan Nowinski}, journal={2006 IEEE Apr 28, 2023 · Other approaches have used GANs for image segmentation, for example, Xue’s Segmentation Adversarial Network (SegAN) , for end-to-end medical image segmentation, using a U-Net network as generator. The well-known semantic segmentation technique is used in medical image analysis to identify and label regions of images. The first category is based on consistency models, which rely on the assumption that minor perturbations in the input should not yield substantial deviations in the corresponding output [6], [9], [10]. We propose a network Nov 22, 2023 · Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training. Non-invasive techniques such as X-ray, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US), capture detailed images of organs, tissues, and abnormalities. In the This repo is a PyTorch-based framework for medical image segmentation, whose goal is to provide an easy-to-use framework for academic researchers to develop and evaluate deep learning models. Diffusion is used to infer the relationships and semantic information between nodes by propagating the information between nodes. Image segmentation is often described as partitioning an image into a finite Oct 14, 2024 · The use of artificial intelligence (AI) in the segmentation of liver structures in medical images has become a popular research focus in the past half-decade. While conventional strategies -- such as domain-specific augmentation, specialized architectures, and tailored training procedures -- can alleviate these issues, they depend on the Nov 15, 2024 · Medical image segmentation is a critical component in the development of computer-aided diagnosis and treatment planning systems. However, both paradigms struggle with the inevitable erroneous predictions from unlabeled data, which poses a risk to task-specific VM-UNetV2: Rethinking Vision Mamba UNet for Medical Image Segmentation: M. The medical image sequences produced by the above techniques provide valuable spatio-temporal characteristics for analysis and segmentation, but the annotation of image sequences is Oct 22, 2019 · The Deep learning model that I will be building in this post is based on this paper U-Net: Convolutional Networks for Biomedical Image Segmentation which still remains state-of-the-art in image segmentation for tasks other than medical images. Moreover, with the remarkable success of pre-trained models in natural language processing Jul 11, 2020 · Why do we need AI for medical image semantic segmentation? Radiotherapy treatment planning requires accurate contours for maximizing target coverage while minimizing the toxicities to the surrounding organs at risk (OARs). The encoder-decoder deep learning framework has been widely applied for numerous medical image segmentation tasks. The widely adopted approach currently is U-Net and its variants. The semantic segmentation aims to divide the images into regions with comparable characteristics, including intensity, homogeneity, and texture. Deep learning-based fully convolution neural networks have played a significant role in the development of automated medical image segmentation models. Second, we discuss the sampling of input-output pairs, thereby highlighting the interaction between voxel-wise Nov 4, 2024 · The data-intensive nature of supervised classification drives the interest of the researchers towards unsupervised approaches, especially for problems such as medical image segmentation, where labeled data is scarce. Methods. First, we discuss the CNN architecture, thereby highlighting the spatial origin of the data, voxel-wise classification and the receptive field. Medical image segmentation based on AI. In this paper, we present a comprehensive thematic survey on medical image segmentation using deep learning techniques. , 2021), surgical navigation (Pan et al. Meanwhile, existing semi-supervised methods that utilize few labeled data alongside a larger amount of unlabeled data are limited to scenarios where the labeled data comprises at least 10% of the total. The performance of AI tools in screening for this task may vary widely and has been tested in the literature in various datasets. With rapid advancements in deep learning methods, conventional U-Net segmentation networks have been applied in many fields. The usage of semantic segmentation in several biomedical applications such as computer-assisted diagnosis (Zhao et al. The task is to cluster the parts of the image side by side, which belong to a class of similar objects [1]. Wang and J. Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model generalization and consistent evaluation across different models. However, the use of DenseNets for 3D image segmentation exhibits the following challenges. , 2016, Sharma and Aggarwal, 2010, Pham et al. Computerized medical image segmentation is a vital tool for diagnosing and treating trendy illnesses. Apr 1, 2024 · Single source domain generalization (SDG) holds promise for more reliable and consistent image segmentation across real-world clinical settings particularly in the medical domain, where data privacy and acquisition cost constraints often limit the availability of diverse datasets. Due to the lack of image detail, it is impossible to derive precise boundaries using image semantic feature information. A typical architecture for segmentation networks is an encoder–decoder structure. (b) Representation of our proposed model’s process: Retrieving the contextual and anatomical information from similar annotated example to guide the foundation models to perform Medical image segmentation aims to divide medical images into specific regions with unique attributes. However, no scientometric report has provided a systematic overview of this scientific area. MSCNNs take benefit Semantic segmentation of medical images is a core task in medical image analysis, where the goal is to classify pixels in an image into corresponding anatomical structures and lesion regions. The other two key functions of the image are to classify the image's surface and define it. It contributes to detecting abnormal areas and providing clinical guid-ance (Chen et al. Convolutional neural network achieved great success in medical image segmentation. While image classification-based explainability techniques have garnered significant attention, their counterparts in semantic segmentation have been relatively neglected. Feb 1, 2023 · These semantic segmentation methods for medical imaging can be categorized into three different classes, namely: (1) Region-Based Segmentation, (2) Fully Convolutional Neural Network (FCN), and (3) Weakly supervised segmentation. Notably, medical im-age segmentation is among the most fundamental and chal- Oct 28, 2024 · Accurate medical image segmentation plays a vital role in clinical practice. Building on the recent advancements of Vision transformers (ViT) in computer vision, we propose an unsupervised segmentation framework using a pre-trained Dino-ViT. **Medical Image Segmentation** is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. Keywords: semantic segmentation, medical image processing, deep learning, fully-convolutional Apr 1, 2023 · Medical image segmentation (Lei et al. Recently, multi-scale convolutional neural networks (MSCNNs) have been extensively used to clear up medical image segmentation tasks. We propose a Jun 25, 2022 · Automatic medical image segmentation is an essential step toward accurate diseases diagnosis and designing a follow-up treatment. , 2000). However, the adoption of pretext tasks in 3D medical imaging has been less Nov 22, 2021 · Semantic segmentation of medical images is also known as pixel-level classification. Furthermore, the Runge–Kutta (RK) methods are powerful tools for building networks from the dynamical systems perspective. Dec 3, 2024 · The popular segmentation techniques are: (1) semantic segmentation: classifies each pixel ; (2) instance segmentation: identifies individual objects ; (3) panoptic segmentation: combines both semantic and instance segmentation, provide a comprehensive understanding of the image. However, it still faces two major challenges. , 2021) and follow-up (Pham et al. For a new segmentation problem, models are typically trained from scratch, requiring substan- Jan 14, 2025 · We outline the evolution of deep learning techniques for medical image segmentation annually from 2015 to 2024 in Fig. This study retrospectively reviews recent studies on the application of deep learning for segmentation tasks in medical imaging and proposes potential directions for Sep 29, 2020 · Semantic object segmentation is a fundamental task in medical image analysis and has been widely used in automatic delineation of regions of interest in 3D medical images, such as cells, tissues or organs. This article Jul 7, 2024 · Aside from offering state-of-the-art performance in medical image generation, denoising diffusion probabilistic models (DPM) can also serve as a representation learner to capture semantic information and potentially be used as an image representation for downstream tasks, e. , 2020b, Milletari et al. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field. Many computer-aided diagnostic systems equipped with deep networks are rapidly reducing human intervention in healthcare. Jan 5, 2025 · Pseudo-labeling and consistency-based co-training are established paradigms in semi-supervised learning. In Mar 1, 2024 · In semantic segmentation, many methods utilize pixel-wise (e. UNET is the deep learning network that segments the critical features. 2, a lightweight multi-modality medical image semantic segmentation network was conducted. , 2021b, Liu et al. When training computer vision models for healthcare use cases, you can use image segmentation as a time and cost-effective approach to labeling and annotation to improve accuracy and outputs. Notably, medical im-age segmentation is among the most fundamental and chal- Medical image segmentation plays an important role in clinical decision making, treatment planning, and disease tracking. This work proposes a methodology to improve the quality of the segmentation by An overview on interactive segmentation techniques for medical images is presented, which takes advantage of automatic segmentation and allow users to intervene the segmentation process by incorporating prior-knowledge, validating results and correcting errors, thus potentially lead to accurate segmentation results. Problems in conventional segmentation models. Jul 23, 2020 · automatic segmentation and measurement from medical images were captured, especially in the fields of intervertebral discs segmentation from 3D MRI scans and wound segmentation from 2D images. [3] This article addresses the issue of medical image semantic segmentation by employing a state-of-the-art Convolutional Neural Network (CNN) model. Mar 4, 2024 · Medical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and treatment. edu Introduction Medical image semantic segmentation (1–3) is a pivotal process in the modern healthcare landscape, playing an indispensable role in diagnosing diseases (4), tracking Jul 15, 2017 · The image semantic segmentation has been extensively studying. , 2022), treatment planning (Sherer et al. Jan 17, 2022 · In Section V, we collect the currently available public medical image segmentation data sets, and summarise limitations of current deep learning methods and future research directions. Russell C. In recent years, a significant number of researchers integrated prevalent deep learning techniques into the realm of medical image segmentation. , 2022, Wang et al. May 10, 2021 · The following post is by Dr. Apr 14, 2021 · Semantic segmentation of medical images provides an important cornerstone for subsequent tasks of image analysis and understanding. Even though many automatic segmentation solutions have been proposed, it is arguably that medical image segmentation Nov 30, 2023 · Diabetic retinopathy is a prevalent eye disease that poses a potential risk of blindness. However Feb 7, 2019 · We propose a new recurrent generative adversarial architecture named RNN-GAN to mitigate imbalance data problem in medical image semantic segmentation where the number of pixels belongs to the desired object are significantly lower than those belonging to the background. This becomes particularly crucial in the context of medical images, where hospitals and research organizations often lack the availability of large datasets. Magnetic Resonance Imaging, Colonoscopy and Ultrasonography) are collected as sequences independently for every patient. 9064: 0. The use of deep learning for image segmentation has become a prevalent trend. Feb 1, 2023 · Semantic-based segmentation (Semseg) methods play an essential part in medical imaging analysis to improve the diagnostic process. This paper provides a comprehensive survey of recent advances in Jul 15, 2022 · Semantic segmentation refers to the process of transforming raw medical images into clinically relevant, spatially structured information, such as outlining tumor boundaries, and is an essential Oct 11, 2024 · The primary task of medical image segmentation is to identify specific regions from these medical images, such as specific organ sites, areas of interest like tumors, etc. Given the prevalent use of image segmentation, ranging from medical to industrial deployments, these techniques warrant a systematic look. In particular, both transformer and convolutional-based architectures have benefit from leveraging pretext tasks for pretraining. a ramification trendy strategies had been proposed to section medical pictures, but most modern them could not acquire excellent accuracy. This layer is a free Considering the wealth of ongoing research on the application of deep learning processing to medical image segmentation, the data volume and practical clinical application problems must be addressed to ensure that the results are properly applied. Sep 2, 2024 · Medical image segmentation | Generative AI | Ultra low-data regimes | End-to-end data generation Correspondence: p1xie@ucsd. Medical image segmenta-tion has been widely studied, with state-of-the-art meth-ods training convolutional neural networks in a super-vised fashion, predicting a label map for a given input im-age [23, 41, 42, 46, 87]. Jan 1, 2024 · As shown in Fig. The study of medical image segmentation usually requires large amounts of well-annotated data to train a deep neural network [10, 13, 14, 17, 20]. Dec 20, 2024 · Medical image segmentation is crucial for diagnostics and treatment planning, yet traditional methods often struggle with the variability of real-world clinical data. xfqyrf pohmprak ggiivi luqvq mvlrgwr bmqvfpo gpjne wjcp eweh rfitxc lmcdtu jckxe hbs bad tgpgx