Segment Boundaries Research Articles

BUA-Net: Boundary and uncertainty-aware attention network for lumbar multi-region magnetic resonance imaging segmentation

Background and Objective:Multi-region segmentation by axial lumbar magnetic resonance imaging (MRI) is important for the diagnosis of lumbar spinal stenosis (LSS) and lumbar disc herniation (LDH) disease. Deep learning-based methods have been widely used in this field, but the segmentation task is still challenging due to intra-class grayscale unevenness, inter-class low contrast and unclear boundaries. Methods:To solve the above problems, we propose a novel boundary and uncertainty-aware attention network (BUA-Net). It is structured around a single encoder and two decoders, each dedicated to either multi-region or boundary segmentation. We use an uncertainty-aware attention (UA) module to extract regions with large segmentation uncertainty and generate an attention mask. The learning and supervisory capabilities of the model are enhanced by a fine seg (FS) module and an attention supervision loss (ASL) in the boundaries and blurred regions. Moreover, a multi-scale spatial channel attention (MSCA) module is proposed that refines the high-level features without increasing any layers. Results:The results of the BUA-Net experiments were tested against seven networks and showed a 0.26% improvement in Dice scores, a 0.86% improvement in precision, and a 0.2% improvement in recall. In metrics related to boundary segmentation effectiveness, our method nearly matches the state-of-the-art in the ASD metric, surpasses it by 2.32 in the 95HD metric, and leads by 2.04, 2.68, and 1.22 in the IVD, TS, and PE categories, respectively. Conclusions:The experimental results demonstrate that our proposed method outperforms other state-of-the-art methods and shows excellent segmentation ability in the boundaries and blurred regions.

W2P: Switching from Weak Supervision to Partial Supervision for Semantic Segmentation

Current weakly-supervised semantic segmentation (WSSS) techniques concentrate on enhancing class activation maps (CAMs) with image-level annotations. Yet, the emphasis on producing these pseudo-labels often overshadows the pivotal role of training the segmentation model itself. This paper underscores the significant influence of noisy pseudo-labels on segmentation network performance, particularly in boundary region. To address above issues, we introduce a novel paradigm: Weak to Partial Supervision (W2P). At its core, W2P categorizes the pseudo-labels from WSSS into two unique supervisions: trustworthy clean labels and uncertain noisy labels. Next, our proposed partially-supervised framework adeptly employs these clean labels to rectify the noisy ones, thereby promoting the continuous enhancement of the segmentation model. To further optimize boundary segmentation, we incorporate a noise detection mechanism that specifically preserves boundary regions while eliminating noise. During the noise refinement phase, we adopt a boundary-conscious noise correction technique to extract comprehensive boundaries from noisy areas. Furthermore, we devise a boundary generation approach that assists in predicting intricate boundary zones. Evaluations on the PASCAL VOC 2012 and MS COCO 2014 datasets confirm our method's impressive segmentation capabilities across various pseudo-labels.

Abstract 887: Fast, interactive, AI-assisted 3D lung tumour segmentation

Abstract Segmentation of lung tumors is an important step in the planning of clinical treatment and operative procedures. Automatic segmentation of advanced lung tumors is challenging due to local invasion and heterogeneous metastasis. Fully automatic deep learning models often fail to accurately delineate advanced disease boundaries and do not allow the user to provide hints to improve segmentation performance. In contrast, interactive segmentation accepts user inputs and is an efficient way to segment lesions while reducing radiologist effort. This paper describes an interactive segmentation approach for lung lesion segmentation in CT scans. Our pipeline is built atop the SwinUNETR neural network architecture and can be expanded to support alternate models. The backbone model accepts radiologists' hints in the form of mouse clicks that indicate whether a location on the scan corresponds to a lesion. Our approach has the following novel contributions: (1) Context-aware encoding of guidance clicks by combining geodesic and Gaussian smoothing, resulting in improved segmentation of lesion boundaries. (2) An iterative prompting strategy to achieve higher accuracy with fewer clicks at inference time. (3) Guidance-aware Conditional Random Fields to refine the produced segmentation masks. (4) Volume cropping around guidance clicks at inference to improve segmentation precision and inference speed. (5) Linearly scaling our approach on multiple GPUs, to improve inference speed. On a public non-small cell lung cancer dataset predominantly containing advanced stage cancers (68\% stage III, and 27\% stage IV), our interactive approach delivers a Dice score of 0.74 with a single click, a 21\% improvement from the fully automatic approach (Dice Score = 0.61), and a Dice score of 0.81 with 5 clicks, which is a 33\% improvement. Inference speed is an important metric for an interactive pipeline. On a typical 350 $\times$ 350 $\times$ 150 voxel chest CT scan volume, our interactive approach infers 5 times faster than the original fully automated approach on a single GPU. After scaling to 4 GPUs, we infer in only 0.5 seconds; 20 times faster than the original approach. Qualitative evaluation from three independent radiologists indicates that our interactive pipeline significantly improved their standard clinical segmentation experience. Citation Format: Mayank Patwari, Yi Wei, Meng Xu, Zhenning Zhang, Konstantinos Sidiropoulos, Balaji Selvaraj, Georgia Hughes, Nadine Garibli, Kamsiriochukwu Ojiako, Mohan Lella, Leon Fedden, James Parkin, Michael Parker, Shaneil Patel, Qin Li, Kedar Patwardhan. Fast, interactive, AI-assisted 3D lung tumour segmentation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 887.

Selective growth of recrystallizing grains into well-characterized deformed aluminum at hardness indentation

The selective 3D growth of multiple recrystallization nuclei/grains around a hardness indentation into a deformed Al grain has been quantified through a new approach that combines two synchrotron techniques: 3D X-ray Laue micro-diffraction and diffraction contrast tomography. The former is used to characterize the deformed microstructure and nucleation, while the latter to track the nuclei's growth during interrupted in situ annealing. The combined 4D (x,y,z,t) datasets enable a detailed quantification of the local deformed microstructure consumed by the nuclei/grains and the boundary characteristics between them, including the stored energy and anisotropy of the orientation distribution of the deformation matrix, as well as the misorientation of the moving and non-moving boundary segments. It is found that only certain nuclei can grow into large sizes, and the growth is heterogeneous both among recrystallizing grains and along different directions. Neither the local stored energy nor the boundary misorientation, whether considered individually or in combination, can fully account for the observed growing and shrinking behavior. The growth difference along different directions is related to the deformation microstructure, specifically the geometrical alignment of dislocation boundaries. Furthermore, the effects of impingement of other recrystallizing grains, recovery of the matrix, and elastic strain/stress, on the growth heterogeneities are discussed in detail with a view towards developing advanced modeling with improved predictability of local recrystallization phenomena. The present study demonstrates the effectiveness of the new approach in enabling 3D analysis of recrystallization in complex materials, with potential applications in the development of advanced engineering materials.

Vibration-assisted vat photopolymerization for pixelated-aliasing-free surface fabrication

Mask image projection-based vat photopolymerization (MIP-VPP) offers advantages like low cost, high resolution, and a wide material range, making it popular in industry and education. Recently, MIP-VPP employing liquid crystal displays (LCDs) has gained traction, increasingly replacing digital micromirror devices, particularly among hobbyists and in educational settings, and is now beginning to be used in industrial environments. However, LCD-based MIP-VPP suffers from pronounced pixelated aliasing arising from LCD’s discrete image pixels and its direct-contact configuration in MIP-VPP machines, leading to rough surfaces on the 3D-printed parts. Here, we propose a vibration-assisted MIP-VPP method that utilizes a microscale vibration to uniformize the light intensity distribution of the LCD-based mask image on VPP’s building platform. By maintaining the same fabrication speed, our technique generates a smoother, non-pixelated mask image, reducing the roughness on flat surfaces and boundary segments of 3D-printed parts. Through light intensity modeling and simulation, we derived an optimal vibration pattern for LCD mask images, subsequently validated by experiments. We assessed the surface texture, boundary integrity, and dimensional accuracy of components produced using the vibration-assisted approach. The notably smoother surfaces and improved boundary roughness enhance the printing quality of MIP-VPP, enabling its promising applications in sectors like the production of 3D-printed optical devices and others.

MF-Net: Multiple-feature extraction network for breast lesion segmentation in ultrasound images

Objective:Breast lesion segmentation in ultrasound images is of great significance for qualitative breast lesions. However, blurred lesion boundaries, irregular lesion shapes, and similar intensity distributions between lesion and background bring challenges to accurately segmenting breast lesions. Recently, several U-Net-based variants and transformer-based networks have been applied in breast lesion segmentation. Nevertheless, these methods have three limitations: (1) Introducing mass attention mechanisms and complex operations, (2) Ignoring the capability of extracting local detail features, and (3) Ignoring fusing different global semantic information. Approach:To alleviate these challenges, we propose a novel multiple-feature extraction network named MF-Net. The core designs are as follows: (1) A transformer-based auxiliary bi-encoder models long-range dependencies, (2) A multiple-feature extraction module excavates the robust local features, and (3) A global feature enhancement module integrates different global context information. Main results:We conducted extensive experiments on three public breast lesion datasets and compared our method with twelve state-of-the-art methods. The results of comparison experiments indicate that our model achieves a 3.16% improvement in boundary accuracy with a slight lead in segmentation accuracy. Similarly, when the boundary accuracy is close, our model also gets a 3.92% improvement in segmentation accuracy. In the generalization experiments, our network also demonstrates varying degrees of superiority in segmentation accuracy and boundary precision. Significance:Our network mainly has three advantages: (1) It achieves outstanding performance in the precise segmentation of the breast lesions, (2) It focuses on exploring details such as irregular morphology and blurred boundaries of breast lesions, providing valuable insights for further clinical research, and (3) It improves performance without decreasing inference speed through the introduction of critical components.

DGCBG-Net: A dual-branch network with global cross-modal interaction and boundary guidance for tumor segmentation in PET/CT images

Background and objectivesAutomatic tumor segmentation plays a crucial role in cancer diagnosis and treatment planning. Computed tomography (CT) and positron emission tomography (PET) are extensively employed for their complementary medical information. However, existing methods ignore bilateral cross-modal interaction of global features during feature extraction, and they underutilize multi-stage tumor boundary features. MethodsTo address these limitations, we propose a dual-branch tumor segmentation network based on global cross-modal interaction and boundary guidance in PET/CT images (DGCBG-Net). DGCBG-Net consists of 1) a global cross-modal interaction module that extracts global contextual information from PET/CT images and promotes bilateral cross-modal interaction of global feature; 2) a shared multi-path downsampling module that learns complementary features from PET/CT modalities to mitigate the impact of misleading features and decrease the loss of discriminative features during downsampling; 3) a boundary prior-guided branch that extracts potential boundary features from CT images at multiple stages, assisting the semantic segmentation branch in improving the accuracy of tumor boundary segmentation. ResultsExtensive experiments are conducted on STS and Hecktor 2022 datasets to evaluate the proposed method. The average Dice scores of our DGCB-Net on the two datasets are 80.33% and 79.29%, with average IOU scores of 67.64% and 70.18%. DGCB-Net outperformed the current state-of-the-art methods with a 1.77% higher Dice score and a 2.12% higher IOU score. ConclusionsExtensive experimental results demonstrate that DGCBG-Net outperforms existing segmentation methods, and is competitive to state-of-arts.

Spoken word recognition using a novel speech boundary segment of voiceless articulatory consonants

Spoken word recognition using a novel speech boundary segment of voiceless articulatory consonants

Improved nested U-structure for accurate nailfold capillary segmentation

Changes in the structure and function of nailfold capillaries may be indicators of numerous diseases. Noninvasive diagnostic tools are commonly used for the extraction of morphological information from segmented nailfold capillaries to study physiological and pathological changes therein. However, current segmentation methods for nailfold capillaries cannot accurately separate capillaries from the background, resulting in issues such as unclear segmentation boundaries. Therefore, improving the accuracy of nailfold capillary segmentation is necessary to facilitate more efficient clinical diagnosis and research. Herein, we propose a nailfold capillary image segmentation method based on a U2-Net backbone network combined with a Transformer structure. This method integrates the U2-Net and Transformer networks to establish a decoder–encoder network, which inserts Transformer layers into the nested two-layer U-shaped architecture of the U2-Net. This structure effectively extracts multiscale features within stages and aggregates multilevel features across stages to generate high-resolution feature maps. The experimental results demonstrate an overall accuracy of 98.23 %, a Dice coefficient of 88.56 %, and an IoU of 80.41 % compared to the ground truth. Furthermore, our proposed method improves the overall accuracy by approximately 2 %, 3 %, and 5 % compared to the original U2-Net, Res-Unet, and U-Net, respectively. These results indicate that the Transformer–U2Net network performs well in nailfold capillary image segmentation and provides more detailed and accurate information on the segmented nailfold capillary structure, which may aid clinicians in the more precise diagnosis and treatment of nailfold capillary-related diseases.

Segmental and Syllabic Articulations: A Descriptive Approach.

This article reports on jaw articulation, in connection with the segmental articulations of tongue tip and lips. The jaw is considered a syllable articulator as it opens and closes for each syllable, and the amount of jaw opening is related to syllable strength, given the same phonemic vowel. Here, we investigate the syllabic and the segmental articulations relationships to the acoustic segments. We analyze electromagnetic articulography data of 18 Southern Swedish speakers by testing hypotheses based on the Descriptive Approach to Segmental Articulations. In this approach, articulatory movements are divided into fast intervals and steady states, and the crucial ("primary") articulators' deceleration and acceleration peaks align with the acoustic segment boundaries. Previous findings on the lips and the tongue tip are confirmed. New results are the jaw as noncrucial ("nonprimary") does not align with the acoustic segment boundaries: Its steady states are shorter than consonant and vowel segments. Moreover, place of articulation of the coda affects the jaw cycle, while syllable type only affects jaw closing. No such effects are found on the edges of the jaw cycle, instead only gender affects jaw deceleration at syllable onset. The jaw is not crucial for the timing of the acoustic segment boundaries; also, syllable borders seem to not be affected by place of segmental articulation. The results strengthen the view of the jaw as syllable articulator providing a frame and the other articulators (e.g., lips and tongue tip) as the segmental articulators providing the content.

On the characterization of rectangular duals

A rectangular partition is a partition of a rectangle into a finite number of rectangles. A rectangular partition is generic if no four of its rectangles meet at the same point. A plane graph G is called a rectangularly dualizable graph if G can be represented as a rectangular partition such that each vertex is represented by a rectangle in the partition and each edge is represented by a common boundary segment shared by the corresponding rectangles. Then the rectangular partition is called a rectangular dual of the RDG. In this paper, we have found a minor error in a characterization for rectangular duals given by Koźmiński and Kinnen in 1985 without formal proof, and we fix this characterization with formal proof.

FUSeg: The Foot Ulcer Segmentation Challenge

Wound care professionals provide proper diagnosis and treatment with heavy reliance on images and image documentation. Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound and provide quantitative measurement for the treatment. Unfortunately, this process is very time-consuming and requires a high level of expertise, hence the need for automatic wound measurement methods. Recently, automatic wound segmentation methods based on deep learning have shown promising performance; yet, they heavily rely on large training datasets. A few wound image datasets were published including the Diabetic Foot Ulcer Challenge dataset, the Medetec wound dataset, and WoundDB. Existing public wound image datasets suffer from small size and a lack of annotation. There is a need to build a fully annotated dataset to benchmark wound segmentation methods. To address these issues, we propose the Foot Ulcer Segmentation Challenge (FUSeg), organized in conjunction with the 2021 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). It contains 1210 pixel-wise annotated foot ulcer images collected over 2 years from 889 patients. The submitted algorithms are reviewed in this paper and the dataset can be accessed through the Foot Ulcer Segmentation Challenge website.

UISS-Net: Underwater Image Semantic Segmentation Network for improving boundary segmentation accuracy of underwater images

UISS-Net: Underwater Image Semantic Segmentation Network for improving boundary segmentation accuracy of underwater images

Research of ordinal model fusion based apple grading

Near infrared spectrum has been applied for the rapid non-destructive prediction and classification of the internal soluble solids content (SSC) of apples, due to its rapid, non-destructive and non-polluting nature. However, current apple grading methods do not make full use of the orderliness relationship existing in the apple grading labels. Therefore, ordinal model is introduced in the issue of apples grading based on the internal SSC. In details, the orderliness in the classification model is considered and ordinal regression is combined with apple classification model to establish ordered partition neural network and ordinal regression extreme learning machine, respectively. Meanwhile, to address the problems of poor applicability of single prediction model and the grading uncertainty associated with compulsory segmentation of grading boundaries, a Gaussian mass function generating method is proposed based on the distance between the predicted ordinal class labels and the real grading boundaries, and the multiple models can be fused through the Dempster combination rule, making a fuller description of the uncertainty problem on ordinal class labels prediction, providing a research basis for online non destructive testing grading of apples. Finally, the multiple model fusion process is fully demonstrated by simulation examples, the grading accuracy of Yantai Red Fuji apples is also improved than single ordinal model.

Vascular changes of the choroid and their correlations with visual acuity in diabetic retinopathy.

To investigate changes in the choroidal vasculature and their correlations with visual acuity in diabetic retinopathy (DR). The cohort was composed of 225 eyes from 225 subjects, including 60 eyes from 60 subjects with healthy control, 55 eyes from 55 subjects without DR, 46 eyes from 46 subjects with nonproliferative diabetic retinopathy (NPDR), 21 eyes from 21 subjects with proliferative diabetic retinopathy (PDR), and 43 eyes from 43 subjects with clinically significant macular edema (CSME). Swept-source optical coherence tomography (SS-OCT) was used to image the eyes with a 12-mm radial line scan protocol. The parameters for 6-mm diameters of region centered on the macular fovea were analyzed. Initially, a custom deep learning algorithm based on a modified residual U-Net architecture was utilized for choroidal boundary segmentation. Subsequently, the SS-OCT image was binarized and the Niblack-based automatic local threshold algorithm was employed to calibrate subfoveal choroidal thickness (SFCT), luminal area (LA), and stromal area (SA) by determining the distance between the two boundaries. Finally, the ratio of LA and total choroidal area (SA + LA) was defined as the choroidal vascularity index (CVI). The choroidal parameters in five groups were compared, and correlations of the choroidal parameters with age, gender, duration of diabetes mellitus (DM), glycated hemoglobin (HbA1c), fasting blood sugar, SFCT and best-corrected visual acuity (BCVA) were analyzed. The CVI, SFCT, LA, and SA values of patients with DR were found to be significantly lower compared to both healthy patients and patients without DR (P< 0.05). The SFCT was significantly higher in NPDR group compared to the No DR group (P < 0.001). Additionally, the SFCT was lower in the PDR group compared to the NPDR group (P = 0.014). Furthermore, there was a gradual decrease in CVI with progression of diabetic retinopathy, reaching its lowest value in the PDR group. However, the CVI of the CSME group exhibited a marginally closer proximity to that of the NPDR group. The multivariate regression analysis revealed a positive correlation between CVI and the duration of DM as well as LA (P < 0.05). The results of both univariate and multivariate regression analyses demonstrated a significant positive correlation between CVI and BCVA (P = 0.003). Choroidal vascular alterations, especially decreased CVI, occurred in patients with DR. The CVI decreased with duration of DM and was correlated with visual impairment, indicating that the CVI might be a reliable imaging biomarker to monitor the progression of DR.

Ancient mural segmentation based on multiscale feature fusion and dual attention enhancement

To address the fuzzy segmentation boundaries, missing details, small target losses and low efficiency of traditional segmentation methods in ancient mural image segmentation scenarios, this paper proposes a mural segmentation model based on multiscale feature fusion and a dual attention-augmented segmentation model (MFAM). The model uses the MobileViT network, which integrates a coordinate attention mechanism, as the feature extraction backbone network. It attains global and local expression capabilities through self-attention, class convolution, and coordinate attention and focuses on location information to expand the receptive field and achieve improved feature extraction efficiency. An A_R_ASPP feature enhancement module is proposed for the attention-optimized residual atrous spatial pyramid pooling module. The module uses residual connections to solve the small target loss problem in murals caused by the excessive sampling rate of atrous convolution and uses a feature attention mechanism to adaptively adjust the feature map weight according to the channel importance levels. A dual attention-enhanced feature fusion module is proposed for multiscale decoder feature fusion to improve the mural segmentation effect. This module uses a cross-level aggregation strategy and an attention mechanism to weight the importance of different feature levels to obtain multilevel semantic feature representations. The model improves the mean intersection over union (MIoU) by 3.06% and the MPA by 1.81% on a mural dataset compared with other models. The model is proven to be effective at improving the segmentation details, efficiency and small target segmentation results produced for mural images, and a new method is proposed for segmenting ancient mural images.

DRANet: A semantic segmentation network for Chinese landscape paintings

With large-scale human-annotated pixel-level annotations, many deep learning-based methods have achieved impressive innovations in the field of semantic image segmentation. However, the lack of publicly available Chinese landscape paintings with pixel-level annotations seriously hinders the process of segmentation of Chinese landscape paintings. In addition, a prominent feature of Chinese landscape paintings is the extensive use of black ink, resulting in a lack of color-related information, and landscape paintings often use blank space to depict sky and water elements, which can lead to confusion about boundary information. These issues make the semantic segmentation of Chinese landscape paintings more challenging. To address these challenges, we construct a Segmented Chinese Landscape Paintings (SegCLP) dataset, which contains 709 images with pixel-level annotations belonging to 6 foreground object classes. Moreover, we propose a novel approach called Decrease Dilation Rate Residual Attention Network (DRANet) to enable accurate semantic segmentation of Chinese landscape paintings. DRANet integrates two primary modules: an attention mechanism and a residual module. The attention module alleviates the training burden associated with the spatial convolution pool pyramid, while the residual block enhances the granularity of the decoder, particularly at the segmentation boundary. Experimental findings corroborate the superior efficacy of DRANet in accurately segmenting Chinese landscape images.

Individual-Tree Segmentation from UAV–LiDAR Data Using a Region-Growing Segmentation and Supervoxel-Weighted Fuzzy Clustering Approach

Accurate individual-tree segmentation is essential for precision forestry. In previous studies, the canopy height model-based method was convenient to process, but its performance was limited owing to the loss of 3D information, and point-based methods usually had high computational costs. Although some hybrid methods have been proposed to solve the above problems, most canopy height model-based methods are used to detect subdominant trees in one coarse crown and disregard the over-segmentation and accurate segmentation of the crown boundaries. This study introduces a combined approach, tested for the first time, for treetop detection and tree crown segmentation using UAV–LiDAR data. First, a multiscale adaptive local maximum filter was proposed to detect treetops accurately, and a Dalponte region-growing method was introduced to achieve crown delineation. Then, based on the coarse-crown result, the mean-shift voxelization and supervoxel-weighted fuzzy c-means clustering method were used to identify the constrained region of each tree. Finally, accurate individual-tree point clouds were obtained. The experiment was conducted using a synthetic uncrewed aerial vehicle (UAV)–LiDAR dataset with 21 approximately 30 × 30 m plots and an actual UAV–LiDAR dataset. To evaluate the performance of the proposed method, the accuracy of the remotely sensed biophysical observations and retrieval frameworks was determined using the tree location, tree height, and crown area. The results show that the proposed method was efficient and outperformed other existing methods.

LD-UNet: A long-distance perceptual model for segmentation of blurred boundaries in medical images

The blurriness of boundaries in medical image target regions hinders further improvement in automatic segmentation accuracy and is a challenging problem. To address this issue, we propose a model called long-distance perceptual UNet (LD-UNet), which has a powerful long-‍distance perception ability and can effectively perceive the semantic context of an entire image. Specifically, LD-UNet utilizes global and local long-distance induction modules, which endow the model with contextual semantic induction capabilities for long-distance feature dependencies. The modules perform long-distance semantic perception at the high and low stages of LD-UNet, respectively, effectively improving the accuracy of local blurred information assessment. We also propose a top-down deep supervision method to enhance the ability of the model to fit data. Then, extensive experiments on four types of tumor data with blurred boundaries are conducted. The dataset includes nasopharyngeal carcinoma, esophageal carcinoma, pancreatic carcinoma, and colorectal carcinoma. The dice similarity coefficient scores obtained by LD-UNet on the four datasets are 73.35%, 85.93%, 70.04%, and 82.71%. Experimental results demonstrate that LD-UNet is more effective in improving the segmentation accuracy of blurred boundary regions than other methods with long-distance perception, such as transformers. Among all models, LD-UNet achieves the best performance. By visualizing the feature dependency field of the models, we further explore the advantages of LD-UNet in segmenting blurred boundaries.

A Hybrid of Fuzzy C-Means for the segmentation in CT scan and X-ray images for screening the COVID-19 patients

In this paper, using CT scan and X-ray images, we present a hybrid approach, based on combining fuzzy C-means with k-means clustering, to evaluate and determine pneumonia infection caused by the coronavirus disease (COVID-19). To achieve this objective, we introduce a hybrid method that combines fuzzy C-means clustering with K-means clustering. This hybrid approach is designed to effectively segment object boundaries within medical images, enabling the precise identification of pneumonia-related features. In addition to our hybrid method, we compare its performance with two other segmentation approaches: the Expectation Maximization (EM) algorithm and 2D Entropy segmentation. Which, the method we propose uses a comparison between the performances of the based on a database of medical imaging test. Experimental results showed that the proposed approach outperforms, it was found that the hybrid fuzzy C-means algorithm segmentation images methods give better performance in terms of accuracy, precision, and F-measure, which is effective in boundaries segmentation. Comparative results of the accuracy and image quality index demonstrate the robustness of AI. It also helps to improve work efficiency with accurate analysis of COVID-19 infection on CT scan and X-rays. In addition, the approach helps radiologists make clinical decisions for diagnosis, follow-up, and prognosis.