Poor Segmentation Research Articles

Multi-Branch Attention Fusion Network for Cloud and Cloud Shadow Segmentation

In remote sensing image processing, the segmentation of clouds and their shadows is a fundamental and vital task. For cloud images, traditional deep learning methods often have weak generalization capabilities and are prone to interference from ground objects and noise, which not only results in poor boundary segmentation but also causes false and missed detections of small targets. To address these issues, we proposed a multi-branch attention fusion network (MAFNet). In the encoder section, the dual branches of ResNet50 and the Swin transformer extract features together. A multi-branch attention fusion module (MAFM) uses positional encoding to add position information. Additionally, multi-branch aggregation attention (MAA) in the MAFM fully fuses the same level of deep features extracted by ResNet50 and the Swin transformer, which enhances the boundary segmentation ability and small target detection capability. To address the challenge of detecting small cloud and shadow targets, an information deep aggregation module (IDAM) was introduced to perform multi-scale deep feature aggregation, which supplements high semantic information, improving small target detection. For the problem of rough segmentation boundaries, a recovery guided module (RGM) was designed in the decoder section, which enables the model to effectively allocate attention to complex boundary information, enhancing the network’s focus on boundary information. Experimental results on the Cloud and Cloud Shadow dataset, HRC-WHU dataset, and SPARCS dataset indicate that MAFNet surpasses existing advanced semantic segmentation techniques.

SEFANet: Semantic enhanced with feature alignment network for semantic segmentation

To address the issues of poor segmentation accuracy and insensitivity to details in semantic segmentation, this paper proposes a novel image semantic segmentation framework SEFANet. Specifically, SEFANet adopts encoder-decoder structure, and incorporates a novel perceptual enhancement mechanism called Multi-scale Spatial Integration Module (MSIM) at the encoder. MSIM is based on group convolution to boost spatial semantic features and refine spatial-gradient semantic features within the multi-scale structure. This module enhances feature extraction across different network levels, leading to improved edge detection and segmentation abilities. In the decoder, SEFANet introduces a pixel-level Interleaved Feature Alignment Module (IFAM), which leverages rich semantic information in low-dimensional features and the strategy of Semantic Offset Field. Meanwhile, IFAM warps the high-dimensional feature map into low-dimensional features, completing the calibration process through convolution operations. Experimental results on the Pascal VOC2012 val dataset and the Cityscapes val dataset confirm the effectiveness and generalization of the proposed semantic segmentation. Additionally, the results further demonstrate that SEFANet improves the poor segmentation accuracy and insensitivity to details, and achieves a competitive performance compared with other semantic segmentation methods.

PAC-UNet: Parallel Dual Self-Attention with Convolution for Meniscal MRI Image Segmentation

Meniscal tears are common sports-injury disorders, and thus accurate segmentation of the meniscus is a worthwhile means of medical diagnosis. However, due to the limited research on image segmentation of the meniscus, most previous works have poor segmentation effects on the meniscus, which affects the adoption of assisted medical diagnosis of meniscus. To address this issue, we propose an improved neural network model, termed as the PAC-UNet model, which introduces a parallel dual self-attention with depth-wise convolution for the meniscus image segmentation. Firstly, in the encoder stage, the parallel block is used to eliminate the negative impact of weak modeling ability caused by weight sharing on shared dimensions, so as to better learn the semantic information. Then, in the decoder stage, the parallel block and patch expansion are used to restore the predicted results of meniscus segmentation by performing self-attention and up-sampling on the features. By analyzing the outcomes of experiments on the meniscus dataset, which were collected from the hospital, it is found that the proposed network performed better than other medical image segmentation networks, where the average Dice Similarity Coefficient (DSC), Intersection Over Union (IOU), precision, and Hausdorff_95 of the medial meniscus and lateral meniscus are up to 89.01%, 80.81%, 90.5%, and 2.43, respectively.

Magnetic Resonance Images Segmentation of Multifidus based on Dense-unet and Superpixel.

Lumbar disc herniation (LDH) is a common clinical condition causing lower back and leg pain. Accurate segmentation of the lumbar discs is crucial for assessing and diagnosing LDH. Magnetic resonance imaging (MRI) can reveal the condition of articular cartilage. However, manual segmentation of MRI images is burdensome for physicians and needs to be more efficient. In this study, we propose a method that combines UNet and superpixel segmentation to address the problem of loss of detailed information in the feature extraction phase, leading to poor segmentation results at object edges. The aim is to provide a reproducible solution for diagnosing patients with lumbar disc herniation. We suggest using the network structure of UNet. Firstly, dense blocks are inserted into the UNet network, and training is performed using the Swish activation function. The Dense-UNet model extracts semantic features from the images and obtains rough semantic segmentation results. Then, an adaptive-scale superpixel segmentation algorithm is applied to segment the input images into superpixel images. Finally, high-level abstract semantic features are fused with the detailed information of the superpixels to obtain edge-optimized semantic segmentation results. Evaluation of a private dataset of multifidus muscles in magnetic resonance images demonstrates that compared to other segmentation algorithms, this algorithm exhibits better semantic segmentation performance in detailed areas such as object edges. Compared to UNet, it achieves a 9.5% improvement in the Dice Similarity Coefficient (DSC) and an 11.3% improvement in the Jaccard Index (JAC). The experimental results indicate that this algorithm improves segmentation performance while reducing computational complexity.

MSFA-Net: A Multiscale Feature Aggregation Network for Semantic Segmentation of Historical Building Point Clouds

In recent years, research on the preservation of historical architecture has gained significant attention, where the effectiveness of semantic segmentation is particularly crucial for subsequent repair, protection, and 3D reconstruction. Given the sparse and uneven nature of large-scale historical building point cloud scenes, most semantic segmentation methods opt to sample representative subsets of points, often leading to the loss of key features and insufficient segmentation accuracy of architectural components. Moreover, the geometric feature information at the junctions of components is cluttered and dense, resulting in poor edge segmentation. Based on this, this paper proposes a unique semantic segmentation network design called MSFA-Net. To obtain multiscale features and suppress irrelevant information, a double attention aggregation module is first introduced. Then, to enhance the model’s robustness and generalization capabilities, a contextual feature enhancement and edge interactive classifier module are proposed to train edge features and fuse the context data. Finally, to evaluate the performance of the proposed model, experiments were conducted on a self-curated ancient building dataset and the S3DIS dataset, achieving OA values of 95.2% and 88.7%, as well as mIoU values of 86.2% and 71.6%, respectively, further confirming the effectiveness and superiority of the proposed method.

A robust quality estimation method for medical image segmentation with small datasets

The quality check of automatic image analysis results is a necessity to eliminate poor outcomes. There are few work on segmentation quality estimation when there is no reference mask available. Generative-based and regression approaches generally have strict assumptions on shape/contrast of anatomical structures, which may fail when there are abnormalities in images or in the presence of domain shift. Ensemble approach promises more generalisability to various types of images; however, they are costly to train. Also, none of these methods were designed for small datasets. To address these shortcomings, this paper presents a segmentation quality estimation method for small datasets with arbitrary dimensions, which is validated with 2D, 3D and 4D image datasets with roughly 20 training images, and on the segmentation of retinal vessel and left atrium segmentation. Although our method uses an ensemble for image segmentation, its design reduces its parameter size. We describe possible scenarios relating the amount of agreement across the base models’ outputs in an ensemble to quality scores; then, present a technique to deal with high quality score estimation for poor segmentation as a result of the base models to largely agree on mistakes. We assess the performance of our method in the presence of different sources of domain shift, and compare it with methods selected from the aforementioned approaches. We found robust quality score estimation, generalisable to different datasets. Our code would be available upon acceptance.

A Pipeline for Evaluation of Machine Learning/Artificial Intelligence Models to Quantify Programmed Death Ligand 1 Immunohistochemistry

Immunohistochemistry (IHC) is used to guide treatment decisions in multiple cancer types. For treatment with checkpoint inhibitors, programmed death ligand 1 (PD-L1) IHC is used as a companion diagnostic. However, the scoring of PD-L1 is complicated by its expression in cancer and immune cells. Separation of cancer and noncancer regions is needed to calculate tumor proportion scores (TPS) of PD-L1, which is based on the percentage of PD-L1-positive cancer cells. Evaluation of PD-L1 expression requires highly experienced pathologists and is often challenging and time-consuming. Here, we used a multi-institutional cohort of 77 lung cancer cases stained centrally with the PD-L1 22C3 clone. We developed a 4-step pipeline for measuring TPS that includes the coregistration of hematoxylin and eosin, PD-L1, and negative control (NC) digital slides for exclusion of necrosis, segmentation of cancer regions, and quantification of PD-L1+ cells. As cancer segmentation is a challenging step for TPS generation, we trained DeepLab V3 in the Visiopharm software package to outline cancer regions in PD-L1 and NC images and evaluated the model performance by mean intersection over union (mIoU) against manual outlines. Only 14 cases were required to accomplish a mIoU of 0.82 for cancer segmentation in hematoxylin-stained NC cases. For PD-L1-stained slides, a model trained on PD-L1 tiles augmented by registered NC tiles achieved a mIoU of 0.79. In segmented cancer regions from whole slide images, the digital TPS achieved an accuracy of 75% against the manual TPS scores from the pathology report. Major reasons for algorithmic inaccuracies include the inclusion of immune cells in cancer outlines and poor nuclear segmentation of cancer cells. Our transparent and stepwise approach and performance metrics can be applied to any IHC assay to provide pathologists with important insights on when to apply and how to evaluate commercial automated IHC scoring systems.

Nonproliferative diabetic retinopathy dataset(NDRD): A database for diabetic retinopathy screening research and deep learning evaluation.

In this article, we provide a database of nonproliferative diabetes retinopathy, which focuses on early diabetes retinopathy with hard exudation, and further explore its clinical application in disease recognition. We collect the photos of nonproliferative diabetes retinopathy taken by Optos Panoramic 200 laser scanning ophthalmoscope, filter out the pictures with poor quality, and label the hard exudative lesions in the images under the guidance of professional medical personnel. To validate the effectiveness of the datasets, five deep learning models are used to perform learning predictions on the datasets. Furthermore, we evaluate the performance of the model using evaluation metrics. Nonproliferative diabetes retinopathy is smaller than proliferative retinopathy and more difficult to identify. The existing segmentation models have poor lesion segmentation performance, while the intersection over union (IOU) value for deep lesion segmentation of models targeting small lesions can reach 66.12%, which is higher than ordinary lesion segmentation models, but there is still a lot of room for improvement. The segmentation of small hard exudative lesions is more challenging than that of large hard exudative lesions. More targeted datasets are needed for model training. Compared with the previous diabetes retina datasets, the NDRD dataset pays more attention to micro lesions.

Medical image segmentation network based on multi-scale frequency domain filter

With the development of deep learning, medical image segmentation in computer-aided diagnosis has become a research hotspot. Recently, UNet and its variants have become the most powerful medical image segmentation methods. However, these methods suffer from (1) insufficient sensing field and insufficient depth; (2) computational nonlinearity and redundancy of channel features; and (3) ignoring the interrelationships among feature channels. These problems lead to poor network segmentation performance and weak generalization ability. Therefore, first of all, we propose an effective replacement scheme of UNet base block, Double residual depthwise atrous convolution (DRDAC) block, to effectively improve the deficiency of receptive field and depth. Secondly, a new linear module, the Multi-scale frequency domain filter (MFDF), is designed to capture global information from the frequency domain. The high order multi-scale relationship is extracted by combining the depthwise atrous separable convolution with the frequency domain filter. Finally, a channel attention called Axial selection channel attention (ASCA) is redesigned to enhance the network’s ability to model feature channel interrelationships. Further, we design a novel frequency domain medical image segmentation baseline method FDFUNet based on the above modules. We conduct extensive experiments on five publicly available medical image datasets and demonstrate that the present method has stronger segmentation performance as well as generalization ability compared to other state-of-the-art baseline methods.

MpMsCFMA-Net: Multi-path Multi-scale Context Feature Mixup and Aggregation Network for medical image segmentation

Automatic and accurate medical image segmentation is a crucial step for clinical diagnosis and treatment planning of diseases. The advanced convolutional neural network (CNN) approaches based on the encoder–decoder structure have achieved state-of-the-art performances in many different medical image segmentation tasks. However, existing networks have insufficient capability of extracting the context information in each encoding stage, so they cannot effectively perceive multi-scale objects in images. In addition, the continuous down-sampling and convolution operations in the encoding stage lead to much loss of the detailed information, resulting in poor segmentation performance. In this paper, we propose a Multi-path Multi-scale Context Feature MixUp and Aggregation Network (named MpMsCFMA-Net) which fuses and aggregates multi-path features with multi-scale context information to address these issues. Based on the encoder–decoder structure, we first design the encoder to encode the semantic and detailed information of input images and introduce multi-scale context extraction module in each encoding stage. Furthermore, we design multiple features mixup module between the encoder and the decoder, aiming at providing different levels of global context information for the decoder by reconstructing skip-connection. Finally, we introduce the decoder with deeper features aggregation to better fuse multi-scale context information across layers. Experimental results on four public medical image datasets confirm that our proposed network achieves promising results and outperforms other state-of-the-art methods in most of evaluation metrics. The source code will be publicly available at https://github.com/tricksterANDthug/MpMsCFMA-Net.

Hybrid channel and spatial attention-UNet for skin lesion segmentation

<span>Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.</span>

An Improved Variational-Based Model for Denoising and Segmentation of Vector-Valued Images

Preserving important features such as edges is one of the main concerns in models for denoising and segmenting vector-valued (colour) images. The Rudin-Osher-Fatemi (ROF) model is a well-known variational-based image denoising model that is capable of reducing image noise while preserving image edges. However, the ROF model is not formulated for denoising colour images and is less effective in preserving corners and weak edges. On the other hand, a variational-based selective segmentation model for colour images called the selective distance segmentation (DSS2) model has recently been proposed, which can effectively partition or extract a specific object in an image. However, the DSS2 model has problems in segmenting colour images with noise, which may result in poor segmentation. Therefore, in this research, we first modify the ROF model to denoise vector-valued images by including the edge detector and extending the formulation into a vector-valued framework. Second, we reformulate the DSS2 model by incorporating the modified ROF model as a new fitting term in the DSS2 model. Peak signal-to-noise ratio (PSNR) is used to measure the image quality, while Jaccard and Dice similarity index are used to evaluate the segmentation quality. The comparison between our proposed model and existing model shows that our model is more effective as indicated by higher PSNR, Jaccard and Dice similarity index values.

EAIS-Former: An efficient and accurate image segmentation method for fruit leaf diseases

Fruit leaf disease segmentation is an essential foundation for achieving accurate disease diagnosis and identification. However, shadows caused by folded leaves and serrations on leaves can lead to difficulty in extracting edge features, affecting the accuracy of leaf segmentation. In addition, the varying shapes and blurred boundaries of disease spots can further lead to poor segmentation performance of spots. To address the above problems, this work proposes a method called EAIS-Former by combining the advantages of global modeling of Transformer, local processing and positional coding of convolutional neural network (CNN) for accurate segmentation in fruit leaf disease images. Dual scale overlap (DSO) patch embedding is designed to effectively extract multi-scale disease features by dual paths to alleviate omission of lesions. Ultra large convolution (ULC) Transformer block is customized for performing positional encoding and global modeling to efficiently extract global and positional features of leaves and diseases. Skip convolutional local optimization (SCLO) module is proposed to optimize the local detail and edge information and improve the pixel classification ability of the model so that the segmentation results of leaves and spots can be finer and more tiny spots can be extracted. Double layer upsampling (DLU) decoder is built to efficiently fuse the detail information with the semantic information and output the accurate segmentation results of leaves and spots. The experimental results show that the proposed method reach 99.04%, 98.64%, 99.24%, 99.42%, 98.59% and 98.58% intersection over union (IoU) for leaf segmentation on apple rust, pomegranate cercospora spot, mango anthracnose, jamun fungal disease, apple alternaria blotch and apple gray spot datasets, respectively. The IoU of lesion segmentation achieve 94.47%, 94.54%, 83.83%, 86.60%, 89.59% and 88.76%, respectively. In contrast to DeepLabv3+, the accuracy of disease segmentation is raised by 5.25%, 5.15%, 5.55%, 7.64%, 7.04% and 9.35%, respectively. Compared with U-Net, the proposed method improves the accuracy of disease spot segmentation by 4.3%, 4.44%, 5.26%, 9.42%, 5.87% and 6.53% under the six fruit leaf test sets, respectively. In addition, total parameters and FLOPs of the proposed method are only 18.44% and 8.47% of U-Net, respectively. Therefore, this study can provide an efficient and accurate method for the task of fruit leaf disease spot segmentation, which provides a sufficient basis for the accurate analysis of fruit leaves and diseases.

Underdog entrepreneurship: a model to predict the success of poor entrepreneurs

Purpose While extensive research exists on how positive life circumstances and positive personality traits promote entrepreneurship, there is little research focusing on how negative personal circumstances may potentially breed entrepreneurship. This study aims to build upon the theory of underdog entrepreneurship to examine the effect of challenging life circumstances such as poverty on the development of entrepreneurial traits in underdog entrepreneurs. This research analyzed the effect of risk-taking (RT) on the entrepreneurial success (ES) of underdog entrepreneurs and the role of optimism (OPM) and persistence (P) as mediating variables and poverty as a moderator variable. Design/methodology/approach A cross-sectional survey was conducted to collect data from 400 micro-finance borrowers of Akhuwat Foundation Pakistan. Akhuwat provides interest-free small loans to the poor segment of society. Preliminary testing of the survey questionnaire with a sample of 35 borrowers of Akhuwat Foundation in Lahore city was conducted before data collection. Data was collected from four randomly selected branches in Lahore city. Findings The authors found that OPM and P significantly mediate the relationship between RT and ES. In addition, the authors analyzed the moderating effect of poverty on the relationship between OPM and ES and found a negative moderating effect. A strong positive moderating effect of poverty was found on the relationship between P and ES. The results of multi-group analysis show that successful and unsuccessful underdog entrepreneurs differ in their RT propensity, which is significantly mediated by OPM and P. Originality/value This study provides empirical evidence on entrepreneurial traits and success of poor entrepreneur in an emerging economy. This work is original and has not been submitted elsewhere.

Otsu Image Segmentation Based on a Fractional Order Moth–Flame Optimization Algorithm

To solve the shortcomings of the Otsu image segmentation algorithm based on traditional Moth–Flame Optimization (MFO), such as its poor segmentation accuracy, slow convergence, and tendency to fall into local optimum, this paper proposes fractional order moth–flame optimization with the Otsu image segmentation algorithm. Utilizing the advantages of memorability and heritability in fractional order differentiation, the position updating of moths is controlled by fractional order. Using the adaptive fractional order, the positions of moths are used to adjust the fractional order adaptively to improve the convergence speed. Combining the improved MFO algorithm with the two-dimensional Otsu algorithm, the optimization objective function is achieved by using its dispersion matrix. The experimental results indicate that, compared with traditional MFO, the convergence rate of the proposed algorithm is improved by about 74.62%. Furthermore, it has better segmentation accuracy and a higher fitness value than traditional MFO.

Optical gas imaging for leak detection based on improved deeplabv3+ model

Optical gas imaging (OGI) is a technique to dynamically observe gas leaks with a wide detection range. As gasses are mobile and diffusive, traditional motion detection methods are difficult to adapt to the change of gas motion state with the environment, resulting in poor segmentation effect and low accuracy. Therefore, this paper proposes a deep learning-based semantic segmentation method of gas for optical gas imaging detection based on DeeplabV3+ model. Firstly, MobileNetV2 is used to replace the original backbone network to reduced model parameters and complexity and facilitates the edge deployment of the model. Second, the dense atrous spatial pyramid pooling (DenseASPP) module is introduced to improve the atrous spatial pyramid pooling (ASPP) module of the model to increase the model sensing field and improve the model gas segmentation accuracy. Finally, Dice loss is used to solve the sample imbalance problem. The experimental results show that IOU and F1 score of the model are improved by 4.10% and 2.47%, and it provides a new intelligent detection method for optical gas imaging.

Context-aware and local-aware fusion with transformer for medical image segmentation

Objective. Convolutional neural networks (CNNs) have made significant progress in medical image segmentation tasks. However, for complex segmentation tasks, CNNs lack the ability to establish long-distance relationships, resulting in poor segmentation performance. The characteristics of intra-class diversity and inter-class similarity in images increase the difficulty of segmentation. Additionally, some focus areas exhibit a scattered distribution, making segmentation even more challenging. Approach. Therefore, this work proposed a new Transformer model, FTransConv, to address the issues of inter-class similarity, intra-class diversity, and scattered distribution in medical image segmentation tasks. To achieve this, three Transformer-CNN modules were designed to extract global and local information, and a full-scale squeeze-excitation module was proposed in the decoder using the idea of full-scale connections. Main results. Without any pre-training, this work verified the effectiveness of FTransConv on three public COVID-19 CT datasets and MoNuSeg. Experiments have shown that FTransConv, which has only 26.98M parameters, outperformed other state-of-the-art models, such as Swin-Unet, TransAttUnet, UCTransNet, LeViT-UNet, TransUNet, UTNet, and SAUNet++. This model achieved the best segmentation performance with a DSC of 83.22% in COVID-19 datasets and 79.47% in MoNuSeg. Significance. This work demonstrated that our method provides a promising solution for regions with high inter-class similarity, intra-class diversity and scatter distribution in image segmentation.

Mesoscale 3D model of irradiated concrete

The concrete biological shield in light-water reactors is exposed to neutron and gamma irradiation, which deteriorates the concrete’s mechanical properties in the long term. To assess the irradiation-induced damage, predictive mechanical models are developed and used in parallel with the characterization of irradiated concrete samples. Realistic 3D simulation domains can drastically improve a model’s prediction. In this work, we utilized x-ray computed tomography (XCT) data of a concrete specimen to reconstruct its 3D microstructure. The XCT data shows low contrast between the concrete’s aggregates and cement paste, resulting in poor image segmentation when using traditional unsupervised techniques. To address this issue, we developed and trained a 2.5D U-Net model on only 24 pre-labeled XCT layers to segment 651 layers of the XCT data. The overall F1-score of the model is approximately 96%. Then, we created a 3D finite element (FE) mesh based on the stack of segmented images. The FE model contains radiation-induced expansion, damage, and creep. The constitutive equations are adapted to each phase (aggregates and cement paste). We simulated the effects of neutron irradiation in the concrete specimen as well as the specimen’s mechanical response to uniaxial compression. Finally, model validation was performed using experimental data on similar concrete specimens in the literature.

Comparative Study on Noise-Estimation-Based Fuzzy C-Means Clustering for Image Segmentation.

Since a noisy image has inferior characteristics, the direct use of Fuzzy C -Means (FCM) to segment it often produces poor image segmentation results. Intuitively, using its ideal value (noise-free image) benefits FCM's robustness enhancement. Therefore, the realization of accurate noise estimation in FCM is a new and important task. To date, only two noise-estimation-based FCM algorithms have been proposed for image segmentation, that is: 1) deviation-sparse FCM (DSFCM) and 2) our earlier proposed residual-driven FCM (RFCM). In this article, we make a thorough comparative study of DSFCM and RFCM. We demonstrate that an RFCM framework can realize more accurate noise estimation than DSFCM when different types of noise are involved. It is mainly thanks to its utilization of noise distribution characteristics instead of noise sparsity used in DSFCM. We show that DSFCM is a particular case of RFCM, thus signifying that they are the same when only impulse noise is involved. With a spatial information constraint, we demonstrate RFCM's superior effectiveness and efficiency over DSFCM in terms of supporting experiments with different levels of single, mixed, and unknown noise.

Ore image segmentation Based on Multiscale Parallel Efficient Channel Attention U-Network

Accurately detecting ore particle size and applying them to the control of semi-autogenous grinding mills are important for improving grinding productivity. Due to the wide range of ore size distribution and mutual adhesion of ores when entering the mill, the current methods based on deep learning image segmentation suffer from significant limitations and poor segmentation effectiveness in practical application. To address this issue, we propose a multi-scale parallel efficient channel attention U-net (MPECA-Unet) segmentation model. The proposed method introduces two-dimensional global attention computation in both spatial and channel domains. It also integrates multiscale fusion information with the encoder's varied scale features to improve segmentation performance for ore image. Experimental results show that the segmentation performance of the model is superior, with smaller error rate when compared to the on-site manual screening results.