Medical Image Segmentation Algorithm Research Articles

Medical image segmentation with UNet-based multi-scale context fusion

Histopathological examination holds a crucial role in cancer grading and serves as a significant reference for devising individualized patient treatment plans in clinical practice. Nevertheless, the distinctive features of numerous histopathological image targets frequently contribute to suboptimal segmentation performance. 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 different encoding stages and assigns different weights to the semantic information at different scales through TBSFF module to improve the learning ability of the network for features. Through multi-scale context fusion and feature selection networks, richer semantic features and detailed information are extracted. The target can be more accurately segmented without significantly increasing the extra overhead. The results demonstrate that our algorithm achieves superior Dice and IoU scores with a relatively small parameter count. Specifically, on the GlaS dataset, the Dice score is 90.56, and IoU is 83.47. For the MoNuSeg dataset, the Dice score is 79.07, and IoU is 65.98.

An enhanced spider wasp optimization algorithm for multilevel thresholding-based medical image segmentation

An enhanced spider wasp optimization algorithm for multilevel thresholding-based medical image segmentation

Integrating PointNet-Based Model and Machine Learning Algorithms for Classification of Rupture Status of IAs.

The rupture of intracranial aneurysms (IAs) would result in subarachnoid hemorrhage with high mortality and disability. Predicting the risk of IAs rupture remains a challenge. This paper proposed an effective method for classifying IAs rupture status by integrating a PointNet-based model and machine learning algorithms. First, medical image segmentation and reconstruction algorithms were applied to 3D Digital Subtraction Angiography (DSA) imaging data to construct three-dimensional IAs geometric models. Geometrical parameters of IAs were then acquired using Geomagic, followed by the computation of hemodynamic clouds and hemodynamic parameters using Computational Fluid Dynamics (CFD). A PointNet-based model was developed to extract different dimensional hemodynamic cloud features. Finally, five types of machine learning algorithms were applied on geometrical parameters, hemodynamic parameters, and hemodynamic cloud features to classify and recognize IAs rupture status. The classification performance of different dimensional hemodynamic cloud features was also compared. The 16-, 32-, 64-, and 1024-dimensional hemodynamic cloud features were extracted with the PointNet-based model, respectively, and the four types of cloud features in combination with the geometrical parameters and hemodynamic parameters were respectively applied to classify the rupture status of IAs. The best classification outcomes were achieved in the case of 16-dimensional hemodynamic cloud features, the accuracy of XGBoost, CatBoost, SVM, LightGBM, and LR algorithms was 0.887, 0.857, 0.854, 0.857, and 0.908, respectively, and the AUCs were 0.917, 0.934, 0.946, 0.920, and 0.944. In contrast, when only utilizing geometrical parameters and hemodynamic parameters, the accuracies were 0.836, 0.816, 0.826, 0.832, and 0.885, respectively, with AUC values of 0.908, 0.922, 0.930, 0.884, and 0.921. In this paper, classification models for IAs rupture status were constructed by integrating a PointNet-based model and machine learning algorithms. Experiments demonstrated that hemodynamic cloud features had a certain contribution weight to the classification of IAs rupture status. When 16-dimensional hemodynamic cloud features were added to the morphological and hemodynamic features, the models achieved the highest classification accuracies and AUCs. Our models and algorithms would provide valuable insights for the clinical diagnosis and treatment of IAs.

Multi-scale medical image segmentation based on pixel encoding and spatial attention mechanism

In response to the issues of single-scale information loss and large model parameter size during the sampling process in U-Net and its variants for medical image segmentation, this paper proposes a multi-scale medical image segmentation method based on pixel encoding and spatial attention. Firstly, by redesigning the input strategy of the Transformer structure, a pixel encoding module is introduced to enable the model to extract global semantic information from multi-scale image features, obtaining richer feature information. Additionally, deformable convolutions are incorporated into the Transformer module to accelerate convergence speed and improve module performance. Secondly, a spatial attention module with residual connections is introduced to allow the model to focus on the foreground information of the fused feature maps. Finally, through ablation experiments, the network is lightweighted to enhance segmentation accuracy and accelerate model convergence. The proposed algorithm achieves satisfactory results on the Synapse dataset, an official public dataset for multi-organ segmentation provided by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), with Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) scores of 77.65 and 18.34, respectively. The experimental results demonstrate that the proposed algorithm can enhance multi-organ segmentation performance, potentially filling the gap in multi-scale medical image segmentation algorithms, and providing assistance for professional physicians in diagnosis.

URCA: Uncertainty-based region clipping algorithm for semi-supervised medical image segmentation

Background and objectiveTraining convolutional neural networks based on large amount of labeled data has made great progress in the field of image segmentation. However, in medical image segmentation tasks, annotating the data is expensive and time-consuming because pixel-level annotation requires experts in the relevant field. Currently, the combination of consistent regularization and pseudo labeling-based semi-supervised methods has shown good performance in image segmentation. However, in the training process, a portion of low-confidence pseudo labels are generated by the model. And the semi-supervised segmentation method still has the problem of distribution bias between labeled and unlabeled data. The objective of this study is to address the challenges of semi-supervised learning and improve the segmentation accuracy of semi-supervised models on medical images. MethodsTo address these issues, we propose an Uncertainty-based Region Clipping Algorithm for semi-supervised medical image segmentation, which consists of two main modules. A module is introduced to compute the uncertainty of two sub-networks predictions with diversity using Monte Carlo Dropout, allowing the model to gradually learn from more reliable targets. To retain model diversity, we use different loss functions for different branches and use Non-Maximum Suppression in one of the branches. The other module is proposed to generate new samples by masking the low-confidence pixels in the original image based on uncertainty information. New samples are fed into the model to facilitate the model to generate pseudo labels with high confidence and enlarge the training data distribution. ResultsComprehensive experiments on the combination of two benchmarks ACDC and BraTS2019 show that our proposed model outperforms state-of-the-art methods in terms of Dice, HD95 and ASD. The results reach an average Dice score of 87.86 % and a HD95 score of 4.214 mm on ACDC dataset. For the brain tumor segmentation, the results reach an average Dice score of 84.79 % and a HD score of 10.13 mm. ConclusionsOur proposed method improves the accuracy of semi-supervised medical image segmentation. Extensive experiments on two public medical image datasets including 2D and 3D modalities demonstrate the superiority of our model. The code is available at: https://github.com/QuintinDong/URCA.

MDER-Net: A Multi-Scale Detail-Enhanced Reverse Attention Network for Semantic Segmentation of Bladder Tumors in Cystoscopy Images

White light cystoscopy is the gold standard for the diagnosis of bladder cancer. Automatic and accurate tumor detection is essential to improve the surgical resection of bladder cancer and reduce tumor recurrence. At present, Transformer-based medical image segmentation algorithms face challenges in restoring fine-grained detail information and local boundary information of features and have limited adaptability to multi-scale features of lesions. To address these issues, we propose a new multi-scale detail-enhanced reverse attention network, MDER-Net, for accurate and robust bladder tumor segmentation. Firstly, we propose a new multi-scale efficient channel attention module (MECA) to process four different levels of features extracted by the PVT v2 encoder to adapt to the multi-scale changes in bladder tumors; secondly, we use the dense aggregation module (DA) to aggregate multi-scale advanced semantic feature information; then, the similarity aggregation module (SAM) is used to fuse multi-scale high-level and low-level features, complementing each other in position and detail information; finally, we propose a new detail-enhanced reverse attention module (DERA) to capture non-salient boundary features and gradually explore supplementing tumor boundary feature information and fine-grained detail information; in addition, we propose a new efficient channel space attention module (ECSA) that enhances local context and improves segmentation performance by suppressing redundant information in low-level features. Extensive experiments on the bladder tumor dataset BtAMU, established in this article, and five publicly available polyp datasets show that MDER-Net outperforms eight state-of-the-art (SOTA) methods in terms of effectiveness, robustness, and generalization ability.

Unsupervised model adaptation for source-free segmentation of medical images

The recent prevalence of deep neural networks has led semantic segmentation networks to achieve human-level performance in the medical field, provided they are given sufficient training data. However, these networks often fail to generalize when tasked with creating semantic maps for out-of-distribution images, necessitating re-training on new distributions. This labor-intensive process requires expert knowledge for generating training labels. In the medical field, distribution shifts can naturally occur due to the choice of imaging devices, such as MRI or CT scanners. To mitigate the need for labeling images in a target domain after successful model training in a fully annotated source domain with a different data distribution, unsupervised domain adaptation (UDA) can be employed. Most UDA approaches ensure target generalization by generating a shared source/target latent feature space, allowing a source-trained classifier to maintain performance in the target domain. However, such approaches necessitate joint source and target data access, potentially leading to privacy leaks with respect to patient information. We propose a UDA algorithm for medical image segmentation that does not require access to source data during adaptation, thereby preserving patient data privacy. Our method relies on approximating the source latent features at the time of adaptation and creates a joint source/target embedding space by minimizing a distributional distance metric based on optimal transport. We demonstrate that our approach is competitive with recent UDA medical segmentation works, even with the added requirement of privacy. 11Early partial results of this work has been presented in 2022 British Machine Vision Conference (Stan and Rostami, 2022a).

Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation.

Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize computer algorithms to identify abnormal features in medical images, assisting radiologists in improving diagnostic accuracy and achieving consistency in image and disease interpretation. Importantly, the quality of medical images, as the target data, determines the achievable level of performance by artificial intelligence algorithms. However, the pixel value range of medical images differs from that of the digital images typically processed via artificial intelligence algorithms, and blindly incorporating such data for training can result in suboptimal algorithm performance. In this study, we propose a medical image-enhancement scheme that integrates generic digital image processing and medical image processing modules. This scheme aims to enhance medical image data by endowing them with high-contrast and smooth characteristics. We conducted experimental testing to demonstrate the effectiveness of this scheme in improving the performance of a medical image segmentation algorithm.

An enhanced chameleon swarm algorithm for global optimization and multi-level thresholding medical image segmentation

An enhanced chameleon swarm algorithm for global optimization and multi-level thresholding medical image segmentation

Interactive segmentation of medical images using deep learning.

Medical image segmentation algorithms based on deep learning have achieved good segmentation results in recent years, but they require a large amount of labeled data. When performing pixel-level labeling on medical images, labeling a target requires marking ten or even hundreds of points along its edge, which requires a lot of time and labor costs. To reduce the labeling cost, we utilize a click-based interactive segmentation method to generate high-quality segmentation labels. However, in current interactive segmentation algorithms, only the interaction information clicked by the user and the image features are fused as the input of the backbone network (so-called early fusion). The early fusion method has the problem that the interactive information is much sparse at this time. Furthermore, the interactive segmentation algorithms do not take into account the boundary problem, resulting in poor model performance. So we propose early fusion and late fusion strategy to prevent the interaction information from being diluted prematurely and make better use of the interaction information. At the same time, we propose a decoupled head structure, by extracting the image boundary information, and combining the boundary loss function to establish the boundary constraint term, so that the network can pay more attention to the boundary information and further improve the performance of the network. Finally, we conduct experiments on three medical datasets (Chaos, VerSe and Uterine Myoma MRI) to verify the effectiveness of our network. The experimental results show that our network greatly improved compared with the baseline, and NoC@80(the number of interactive clicks over 80% of the IoU threshold) improved by 0.1, 0.1, and 0.2. In particular, we have achieved a NoC@80 score of 1.69 on Chaos. According to statistics, manual annotation takes 25 min to label a case(Uterine Myoma MRI). Annotating a medical image with our method can be done in only 2 or 3 clicks, which can save more than 50% of the cost.

Medical Image Segmentation Algorithm Based on Markup Improvement Combining the Watershed Algorithm and the Level Set Algorithm

Medical Image Segmentation Algorithm Based on Markup Improvement Combining the Watershed Algorithm and the Level Set Algorithm

基于Swin-Unet改进的医学图像分割算法

基于Swin-Unet改进的医学图像分割算法

Research progress on medical ultrasound image segmentation algorithms

Medical ultrasound imaging is an integral part of preoperative diagnosis, lesion screening and ultrasound-guided interventional surgeries. Image segmentation techniques can enhance the identification of lesions and separate them from complex backgrounds, aiding physicians in both quantitative and qualitative analyses. Ultrasound image segmentation algorithms are primarily categorized into two types: traditional non-semantic segmentation and deep learning-based semantic segmentation, each with distinct advantages and drawbacks. This paper delves into these segmentation principles, elucidating their relevance in the realm of ultrasound image segmentation, and offers an overview of current research trends. Our goal is to provide guidance for physicians and researchers in selecting the most suitable segmentation algorithm that tailors to their specific requirements.

Development of a Medical Image Segmentation Algorithm based on Fuzzy C-Means Clustering

Breast mass segmentation in mammography plays a very important role in computer-aided diagnosis (CAD) systems. In this article, we propose a mammography image segmentation method based on a combined approach. The fuzzy clustering method and thresholding segmentation. Subsequently, we use the wavelet transform and the Canny filter for edge detection.

MD-UNet: a medical image segmentation network based on mixed depthwise convolution.

In the process of cancer diagnosis and treatment, accurate extraction of the lesion area helps the doctor to judge the condition. Currently, medical image segmentation algorithms based on UNet have been verified to be able to play an important role in clinical diagnosis. However, these methods still have the following drawbacks in extracting the region of interest (ROI): (1) ignoring the intra-class variability of medical images. (2) Failure to obtain effective feature redundancy. To address these problems, a U-shaped medical image segmentation network based on a Mixed depthwise convolution residual module (MDRM), called MD-UNet, is proposed in this paper. In MD-UNet, the MDRM built with a Mixed depthwise convolution attention block (MDAB) captures both local and global dependencies in the image to mitigate the effects of intra-class differences. MDAB captures valid redundant features and further captures global features of the input data. At the same time, the lightweight MDAB senses changes in the receptive field and generates multiple feature mappings. Compared with UNeXt on the ISIC2018 dataset, the MD-UNet segmentation accuracy Dice and IoU are improved by 1.33% and 1.91%, respectively. The code is available at https://github.com/Cloud-Liu/MD-UNet .

MCAFNet: multiscale cross-layer attention fusion network for honeycomb lung lesion segmentation.

Accurate segmentation of honeycomb lung lesions from lung CT images plays a crucial role in the diagnosis and treatment of various lung diseases. However, the availability of algorithms for automatic segmentation of honeycomb lung lesions remains limited. In this study, we propose a novel multi-scale cross-layer attention fusion network (MCAFNet) specifically designed for the segmentation of honeycomb lung lesions, taking into account their shape specificity and similarity to surrounding vascular shadows. The MCAFNet incorporates several key modules to enhance the segmentation performance. Firstly, a multiscale aggregation (MIA) module is introduced in the input part to preserve spatial information during downsampling. Secondly, a cross-layer attention fusion (CAF) module is proposed to capture multiscale features by integrating channel information and spatial information from different layers of the feature maps. Lastly, a bidirectional attention gate (BAG) module is constructed within the skip connection to enhance the model's ability to filter out background information and focus on the segmentation target. Experimental results demonstrate the effectiveness of the proposed MCAFNet. On the honeycomb lung segmentation dataset, the network achieves an Intersection over Union (IoU) of 0.895, mean IoU (mIoU) of 0.921, and mean Dice coefficient (mDice) of 0.949, outperforming existing medical image segmentation algorithms. Furthermore, experiments conducted on additional datasets confirm the generalizability and robustness of the proposed model. The contribution of this study lies in the development of the MCAFNet, which addresses the lack of automated segmentation algorithms for honeycomb lung lesions. The proposed network demonstrates superior performance in accurately segmenting honeycomb lung lesions, thereby facilitating the diagnosis and treatment of lung diseases. This work contributes to the existing literature by presenting a novel approach that effectively combines multi-scale features and attention mechanisms for lung lesion segmentation. The code is available at https://github.com/Oran9er/MCAFNet .

Transformer guided self-adaptive network for multi-scale skin lesion image segmentation

BackgroundIn recent years, skin lesion has become a major public health concern, and the diagnosis and management of skin lesions depend heavily on the correct segmentation of the lesions. Traditional convolutional neural networks (CNNs) have demonstrated promising results in skin lesion segmentation, but they are limited in their ability to capture distant connections and intricate features. In addition, current medical image segmentation algorithms rarely consider the distribution of different categories in different regions of the image and do not consider the spatial relationship between pixels. ObjectivesThis study proposes a self-adaptive position-aware skin lesion segmentation model SapFormer to capture global context and fine-grained detail, better capture spatial relationships, and adapt to different positional characteristics. The SapFormer is a multi-scale dynamic position-aware structure designed to provide a more flexible representation of the relationships between skin lesion characteristics and lesion distribution. Additionally, it increases skin lesion segmentation accuracy and decreases incorrect segmentation of non-lesion areas. InnovationsSapFormer designs multiple hybrid transformers for multi-scale feature encoding of skin images and multi-scale positional feature sensing of the encoded features using a transformer decoder to obtain fine-grained features of the lesion area and optimize the regional feature distribution. The self-adaptive feature framework, built upon the transformer decoder module, dynamically and automatically generates parameterizations with learnable properties at different positions. These parameterizations are derived from the multi-scale encoding characteristics of the input image. Simultaneously, this paper utilizes the cross-attention network to optimize the features of the current region according to the features of other regions, aiming to increase skin lesion segmentation accuracy. Main resultsThe ISIC-2016, ISIC-2017, and ISIC-2018 datasets for skin lesions are used as the basis for the experiment. On these datasets, the proposed model has accuracy values of 97.9 %, 94.3 %, and 95.7 %, respectively. The proposed model's IOU values are, in order, 93.2 %, 86.4 %, and 89.4 %. The proposed model's DSC values are 96.4 %, 92.6 %, and 94.3 %, respectively. All three metrics surpass the performance of the majority of state-of-the-art (SOTA) models. SapFormer's metrics on these datasets demonstrate that it can precisely segment skin lesions. Notably, our approach exhibits remarkable noise resistance in non-lesion areas, while simultaneously conducting finer-grained regional feature extraction on the skin lesion image. ConclusionsIn conclusion, the integration of a transformer-guided position-aware network into semantic skin lesion segmentation results in a notable performance boost. The ability of our proposed network to capture spatial relationships and fine-grained details proves beneficial for effective skin lesion segmentation. By enhancing lesion localization, feature extraction, quantitative analysis, and classification accuracy, the proposed segmentation model improves the diagnostic efficiency of skin lesion analysis on dermoscopic images. It assists dermatologists in making more accurate and efficient diagnoses, ultimately leading to better patient care and outcomes. This research paves the way for advances in diagnosing and treating skin lesions, promoting better understanding and decision-making in the clinical setting.

Automatic Construction of U-Net NetworkBased on Genetic Algorithm for Medical Image Segmentation

Automatic Construction of U-Net NetworkBased on Genetic Algorithm for Medical Image Segmentation

Improved African vultures optimization algorithm for medical image segmentation

Improved African vultures optimization algorithm for medical image segmentation

EU-Net: Automatic U-Net neural architecture search with differential evolutionary algorithm for medical image segmentation

Medical images are crucial in clinical practice, providing essential information for patient assessment and treatment planning. However, manual extraction of information from images is both time-consuming and prone to errors. The emergence of U-Net addresses this challenge by automating the segmentation of anatomical structures and pathological lesions in medical images, thereby significantly enhancing the accuracy of image interpretation and diagnosis. However, the performance of U-Net largely depends on its encoder–decoder structure, which requires researchers with knowledge of neural network architecture design and an in-depth understanding of medical images. In this paper, we propose an automatic U-Net Neural Architecture Search (NAS) algorithm using the differential evolutionary (DE) algorithm, named EU-Net, to segment critical information in medical images to assist physicians in diagnosis. Specifically, by presenting the variable-length strategy, the proposed EU-Net algorithm can sufficiently and automatically search for the neural network architecture without expertise. Moreover, the utilization of crossover, mutation, and selection strategies of DE takes account of the trade-off between exploration and exploitation in the search space. Finally, in the encoding and decoding phases of the proposed algorithm, different block-based and layer-based structures are introduced for architectural optimization. The proposed EU-Net algorithm is validated on two widely used medical datasets, i.e., CHAOS and BUSI, for image segmentation tasks. Extensive experimental results show that the proposed EU-Net algorithm outperforms the chosen peer competitors in both two datasets. In particular, compared to the original U-Net, our proposed method improves the metric mIou by at least 6%.