Deep Learning-based Segmentation Methods Research Articles

Effects of different ground segmentation methods on the accuracy of UAV-based canopy volume measurements.

The nonuniform distribution of fruit tree canopies in space poses a challenge for precision management. In recent years, with the development of Structure from Motion (SFM) technology, unmanned aerial vehicle (UAV) remote sensing has been widely used to measure canopy features in orchards to balance efficiency and accuracy. A pipeline of canopy volume measurement based on UAV remote sensing was developed, in which RGB and digital surface model (DSM) orthophotos were constructed from captured RGB images, and then the canopy was segmented using U-Net, OTSU, and RANSAC methods, and the volume was calculated. The accuracy of the segmentation and the canopy volume measurement were compared. The results show that the U-Net trained with RGB and DSM achieves the best accuracy in the segmentation task, with mean intersection of concatenation (MIoU) of 84.75% and mean pixel accuracy (MPA) of 92.58%. However, in the canopy volume estimation task, the U-Net trained with DSM only achieved the best accuracy with Root mean square error (RMSE) of 0.410 m3, relative root mean square error (rRMSE) of 6.40%, and mean absolute percentage error (MAPE) of 4.74%. The deep learning-based segmentation method achieved higher accuracy in both the segmentation task and the canopy volume measurement task. For canopy volumes up to 7.50 m3, OTSU and RANSAC achieve an RMSE of 0.521 m3 and 0.580 m3, respectively. Therefore, in the case of manually labeled datasets, the use of U-Net to segment the canopy region can achieve higher accuracy of canopy volume measurement. If it is difficult to cover the cost of data labeling, ground segmentation using partitioned OTSU can yield more accurate canopy volumes than RANSAC.

An automated two-stage approach to kidney and tumor segmentation in CT imaging.

The incidence of kidney tumors is progressively increasing each year. The precision of segmentation for kidney tumors is crucial for diagnosis and treatment. To enhance accuracy and reduce manual involvement, propose a deep learning-based method for the automatic segmentation of kidneys and kidney tumors in CT images. The proposed method comprises two parts: object detection and segmentation. We first use a model to detect the position of the kidney, then narrow the segmentation range, and finally use an attentional recurrent residual convolutional network for segmentation. Our model achieved a kidney dice score of 0.951 and a tumor dice score of 0.895 on the KiTS19 dataset. Experimental results show that our model significantly improves the accuracy of kidney and kidney tumor segmentation and outperforms other advanced methods. The proposed method provides an efficient and automatic solution for accurately segmenting kidneys and renal tumors on CT images. Additionally, this study can assist radiologists in assessing patients' conditions and making informed treatment decisions.

Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations.

Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images.

Comparative assessment of established and deep learning-based segmentation methods for hippocampal volume estimation in brain magnetic resonance imaging analysis.

In this study, our objective was to assess the performance of two deep learning-based hippocampal segmentation methods, SynthSeg and TigerBx, which are readily available to the public. We contrasted their performance with that of two established techniques, FreeSurfer-Aseg and FSL-FIRST, using three-dimensional T1-weighted MRI scans (n = 1447) procured from public databases. Our evaluation focused on the accuracy and reproducibility of these tools in estimating hippocampal volume. The findings suggest that both SynthSeg and TigerBx are on a par with Aseg and FIRST in terms of segmentation accuracy and reproducibility, but offer a significant advantage in processing speed, generating results in less than 1 min compared with several minutes to hours for the latter tools. In terms of Alzheimer's disease classification based on the hippocampal atrophy rate, SynthSeg and TigerBx exhibited superior performance. In conclusion, we evaluated the capabilities of two deep learning-based segmentation techniques. The results underscore their potential value in clinical and research environments, particularly when investigating neurological conditions associated with hippocampal structures.

Feature Sparse Choosing VIT Model for Efficient Concrete Crack Segmentation in Portable Crack Measuring Devices

Concrete crack measurement is important for concrete buildings. Deep learning-based segmentation methods have achieved state-of-art results. However, the model size of these models is extremely large which is impossible to use in portable crack measuring devices. To address this problem, a light-weight concrete crack segmentation model based on the Feature Sparse Choosing VIT (LTNet) is proposed by us. In our proposed model, a Feature Sparse Choosing VIT (FSVIT) is used to reduce computational complexity in VIT as well as reducing the number of channels for crack features. In addition, a Feature Channel Selecting Module (FCSM) is proposed by us to reduce channel features as well as suppressing the influence of interfering features. Finally, Depthwise Separable Convolutions are used to substitute traditional convolutions for further reducing computational complexity. As a result, the model size of our LTNet is extremely small. Experimental results show that our LTNet could achieve an accuracy of 0.887, 0.817 and 0.693, and achieve a recall of 0.882, 0.805 and 0.681 on three datasets, respectively, which is 3–8% higher than current mainstream algorithms. However, the model size of our LTNet is only 2 M.

Estimation of Damage Levels in Masonry Structures Following Earthquake Impact Using Deep Learning-based Segmentation Method

This study presents an innovative and automated methodology for assessing damage levels in masonry structures following earthquakes, utilizing a deep learning-based segmentation approach. Central to this research is the use of a U-Net convolutional neural network (CNN) model, which facilitates automated damage detection with a focus on smartphone-enabled, real-time analysis. A key feature of this method is a novel damage index (DI), calculated by normalizing the surface area of detected damages against the total area of the structure, as viewed from the same perspective. The findings indicate a marked improvement in damage detection capabilities, with the U-Net model achieving a precision of approximately 92% and a recall of around 93%. These figures highlight the model’s proficiency in accurately identifying damaged areas and reducing false positives, an essential aspect of post-earthquake evaluations. While this methodology represents a significant step forward in enabling rapid and cost-effective post-earthquake inspections, it is accompanied by certain constraints, particularly in terms of dataset diversity and computational requirements. Despite these challenges, the high accuracy and effectiveness of the damage detection and indexing process demonstrate strong potential for future applications in structural health monitoring, especially in scenarios that demand prompt action and are limited by resources.

Comparative Analysis of Baseline Vnet and Unet Architectures on Pancreas Segmentation

The pancreas is one of the vital organs in the human body. It has an essential role in the digestive system and endocrine system. Diseases such as cancer, diabetes, hormonal problems, pancreatitis, and digestive problems occur in pancreatic disorders. In detecting pancreatic disorders, first blood and urine tests are requested. If further examination is needed, CT (Computed Tomography), MR (Magnetic Resonance), and EUS (Endoscopic Ultrasonography) imaging methods are used. Pancreas segmentation is generally the process of defining and drawing the lines of the pancreas from medical images such as CT and MRI. The size and shape of the pancreas varies from person to person. Manual segmentation of the pancreas is time-consuming and varies between physicians. Recently, deep learning-based segmentation methods that achieve high-performance results in organ segmentation have become trendy. In this study, Unet and Vnet architectures were comparatively analyzed on the NIH-CT-82 dataset. As a result of the ablation studies, a validation sensitivity of 0.9978 and a validation loss of 0.041 were obtained in the Unet architecture. In the training with the Vnet architecture, 0.9975 validation sensitivity and 0.046 validation loss values were obtained, respectively.

Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation

Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain, especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarize both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review can inspire the research community to explore solutions to this challenge and further advance the field of medical image segmentation.

Semi-Supervised Representation Learning for Segmentation on Medical Volumes and Sequences.

Benefiting from the massive labeled samples, deep learning-based segmentation methods have achieved great success for two dimensional natural images. However, it is still a challenging task to segment high dimensional medical volumes and sequences, due to the considerable efforts for clinical expertise to make large scale annotations. Self/semi-supervised learning methods have been shown to improve the performance by exploiting unlabeled data. However, they are still lack of mining local semantic discrimination and exploitation of volume/sequence structures. In this work, we propose a semi-supervised representation learning method with two novel modules to enhance the features in the encoder and decoder, respectively. For the encoder, based on the continuity between slices/frames and the common spatial layout of organs across subjects, we propose an asymmetric network with an attention-guided predictor to enable prediction between feature maps of different slices of unlabeled data. For the decoder, based on the semantic consistency between labeled data and unlabeled data, we introduce a novel semantic contrastive learning to regularize the feature maps in the decoder. The two parts are trained jointly with both labeled and unlabeled volumes/sequences in a semi-supervised manner. When evaluated on three benchmark datasets of medical volumes and sequences, our model outperforms existing methods with a large margin of 7.3% DSC on ACDC, 6.5% on Prostate, and 3.2% on CAMUS when only a few labeled data is available. Further, results on the M&M dataset show that the proposed method yields improvement without using any domain adaption techniques for data from unknown domain. Intensive evaluations reveal the effectiveness of representation mining, and superiority on performance of our method. The code is available at https://github.com/CcchenzJ/BootstrapRepresentation.

FastCellpose: A Fast and Accurate Deep-Learning Framework for Segmentation of All Glomeruli in Mouse Whole-Kidney Microscopic Optical Images.

Automated evaluation of all glomeruli throughout the whole kidney is essential for the comprehensive study of kidney function as well as understanding the mechanisms of kidney disease and development. The emerging large-volume microscopic optical imaging techniques allow for the acquisition of mouse whole-kidney 3D datasets at a high resolution. However, fast and accurate analysis of massive imaging data remains a challenge. Here, we propose a deep learning-based segmentation method called FastCellpose to efficiently segment all glomeruli in whole mouse kidneys. Our framework is based on Cellpose, with comprehensive optimization in network architecture and the mask reconstruction process. By means of visual and quantitative analysis, we demonstrate that FastCellpose can achieve superior segmentation performance compared to other state-of-the-art cellular segmentation methods, and the processing speed was 12-fold higher than before. Based on this high-performance framework, we quantitatively analyzed the development changes of mouse glomeruli from birth to maturity, which is promising in terms of providing new insights for research on kidney development and function.

A dynamic semantic segmentation algorithm with encoder-crossor-decoder structure for pixel-level building cracks

A large number of newly built infrastructures as well as those constructed in the early stage are faced with the problems of detection and maintenance. However, it is difficult to detect building cracks because of its small size and complex background noise. In this study, a crack segmentation network based on Encoder-Crossor-Decoder structure is innovatively proposed to solve the problems of small cracks and easy to be disturbed by background. Then, a loss function is proposed to address the problem of large differences in the ratio of cracks to background pixels in architectural crack segmentation. The experiments show that the loss function can effectively improve the training effect of the model and make the model obtain better semantic segmentation ability. Finally, according to the requirements of building crack detection, a large dataset of concrete pavement cracks is produced, which fills the gap of large dataset of semantic segmentation of cracks. The excellent effect of the model and loss function is verified with three datasets containing most of the major material and structural scenes. In addition, we compare the model with other deep learning segmentation models to validate its effectiveness. The results show that the mIoU of the model of this study reaches 84.04%, 77.56% and 87.38% in the bridge non-steel crack dataset, steel surface crack dataset and our concrete crack dataset, respectively. The accuracy reaches 99.14%, 98.62% and 99.37%. F1 reaches 0.911, 0.873 and 0.963 respectively. It outperforms other deep learning based segmentation methods.

GA-Net: Ghost convolution adaptive fusion skin lesion segmentation network

Automatic segmentation of skin lesions is a pivotal task in computer-aided diagnosis, playing a crucial role in the early detection and treatment of skin cancer. Despite the existence of numerous deep learning-based segmentation methods, the extraction of lesion features remains inadequate as a result of the segmentation process. Consequently, skin lesion image segmentation continues to face challenges regarding missing detailed information and inaccurate segmentation of the lesion region. In this paper, we propose a ghost convolution adaptive fusion network for skin lesion segmentation. First, the neural network incorporates a ghost module instead of the ordinary convolution layer, generating a rich skin lesion feature map for comprehensive target feature extraction. Subsequently, the network employs an adaptive fusion module and bilateral attention module to connect the encoding and decoding layers, facilitating the integration of shallow and deep network information. Moreover, multi-level output patterns are used for pixel prediction. Layer feature fusion effectively combines output features of different scales, thus improving image segmentation accuracy. The proposed network was extensively evaluated on three publicly available datasets: ISIC2016, ISIC2017, and ISIC2018. The experimental results demonstrated accuracies of 96.42%, 94.07%, and 95.03%, and kappa coefficients of 90.41%, 81.08%, and 86.96%, respectively. The overall performance of our network surpassed that of existing networks. Simulation experiments further revealed that the ghost convolution adaptive fusion network exhibited superior segmentation results for skin lesion images, offering new possibilities for the diagnosis of skin diseases.

Automatic brain structure segmentation for 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance images via deep learning.

Brain structure segmentation is of great value in diagnosing brain disorders, allowing radiologists to quickly acquire regions of interest and assist in subsequent analyses, diagnoses and treatment. Current brain structure segmentation methods are usually applied to magnetic resonance (MR) images, which provide higher soft tissue contrast and better spatial resolution. However, fewer segmentation methods are conducted on a positron emission tomography/magnetic resonance imaging (PET/MRI) system that combines functional and structural information to improve analysis accuracy. In this paper, we explore a dual-modality image segmentation model to segment brain 18F-fluorodeoxyglucose (18F-FDG) PET/MR images based on the U-Net architecture. This model takes registered PET and MR images as parallel inputs, and four evaluation metrics (Dice score, Jaccard coefficient, precision and sensitivity) are used to evaluate segmentation performance. Moreover, we also compared the proposed approach with other single-modality segmentation strategies, including PET-only segmentation and MRI-only segmentation. The experiments were conducted on the clinical head data of 120 patients, and the results show that the proposed algorithm accurately delineates brain volumes of interest (VOIs), achieving superior performance with 84.24%±1.44% Dice score, 74.36%±2.40% Jaccard, 84.33%±1.56% precision and 84.73%±1.56% sensitivity. Furthermore, compared with directly using the FreeSurfer toolkit, the proposed method reduced the segmentation time, which only needs 20 seconds to segment the whole brain for each patient. We present a deep learning-based method for the joint segmentation of anatomical and functional PET/MR images. Compared with other single-modality methods, our method greatly improved the accuracy of brain structure delineation, which shows great potential for brain analysis.

Automated segmentation of the human supraclavicular fat depot via deep neural network in water-fat separated magnetic resonance images.

Human brown adipose tissue (BAT), mostly located in the cervical/supraclavicular region, is a promising target in obesity treatment. Magnetic resonance imaging (MRI) allows for mapping the fat content quantitatively. However, due to the complex heterogeneous distribution of BAT, it has been difficult to establish a standardized segmentation routine based on magnetic resonance (MR) images. Here, we suggest using a multi-modal deep neural network to detect the supraclavicular fat pocket. A total of 50 healthy subjects [median age/body mass index (BMI) =36 years/24.3 kg/m2] underwent MRI scans of the neck region on a 3 T Ingenia scanner (Philips Healthcare, Best, Netherlands). Manual segmentations following fixed rules for anatomical borders were used as ground truth labels. A deep learning-based method (termed as BAT-Net) was proposed for the segmentation of BAT on MRI scans. It jointly leveraged two-dimensional (2D) and three-dimensional (3D) convolutional neural network (CNN) architectures to efficiently encode the multi-modal and 3D context information from multi-modal MRI scans of the supraclavicular region. We compared the performance of BAT-Net to that of 2D U-Net and 3D U-Net. For 2D U-Net, we analyzed the performance difference of implementing 2D U-Net in three different planes, denoted as 2D U-Net (axial), 2D U-Net (coronal), and 2D U-Net (sagittal). The proposed model achieved an average dice similarity coefficient (DSC) of 0.878 with a standard deviation of 0.020. The volume segmented by the network was smaller compared to the ground truth labels by 9.20 mL on average with a mean absolute increase in proton density fat fraction (PDFF) inside the segmented regions of 1.19 percentage points. The BAT-Net outperformed all implemented 2D U-Nets and the 3D U-Nets with average DSC enhancement ranging from 0.016 to 0.023. The current work integrates a deep neural network-based segmentation into the automated segmentation of supraclavicular fat depot for quantitative evaluation of BAT. Experiments show that the presented multi-modal method benefits from leveraging both 2D and 3D CNN architecture and outperforms the independent use of 2D or 3D networks. Deep learning-based segmentation methods show potential towards a fully automated segmentation of the supraclavicular fat depot.

Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology.

Automatic nuclear segmentation in digital microscopic tissue images can aid pathologists to extract high-quality features for nuclear morphometrics and other analyses. However, image segmentation is a challenging task in medical image processing and analysis. This study aimed to develop a deep learning-based method for nuclei segmentation of histological images for computational pathology. The original U-Net model sometime has a caveat in exploring significant features. Herein, we present the Densely Convolutional Spatial Attention Network (DCSA-Net) model based on U-Net to perform the segmentation task. Furthermore, the developed model was tested on external multi-tissue dataset - MoNuSeg. To develop deep learning algorithms for well-segmenting nuclei, a large quantity of data are mandatory, which is expensive and less feasible. We collected hematoxylin and eosin-stained image data sets from two hospitals to train the model with a variety of nuclear appearances. Because of the limited number of annotated pathology images, we introduced a small publicly accessible data set of prostate cancer (PCa) with more than 16,000 labeled nuclei. Nevertheless, to construct our proposed model, we developed the DCSA module, an attention mechanism for capturing useful information from raw images. We also used several other artificial intelligence-based segmentation methods and tools to compare their results to our proposed technique. To prioritize the performance of nuclei segmentation, we evaluated the model's outputs based on the Accuracy, Dice coefficient (DC), and Jaccard coefficient (JC) scores. The proposed technique outperformed the other methods and achieved superior nuclei segmentation with accuracy, DC, and JC of 96.4% (95% confidence interval [CI]: 96.2 - 96.6), 81.8 (95% CI: 80.8 - 83.0), and 69.3 (95% CI: 68.2 - 70.0), respectively, on the internal test data set. Our proposed method demonstrates superior performance in segmenting cell nuclei of histological images from internal and external datasets, and outperforms many standard segmentation algorithms used for comparative analysis.

Automated Segmentation of Dental CBCT Image Using an Improved U-Net Network

In the field of clinical dental medicine, Cone Beam Computed Tomography (CBCT) is a useful tool for the measurement of various dimensions related to the oral cavity, including height and thickness. This provides invaluable guidance and reference for risk assessment in orthodontic treatment, selection of treatment plans and implant treatment. However, segmentation of the teeth region from CBCT images is a daunting task due to complex root morphology and indistinct boundaries between the root and the alveolar bone. Manual annotation of the teeth area is resource-intensive, and deep learning-based segmentation methods are susceptible to noise, reducing their efficiency. To tackle these complexities, a multi-filter attention module is proposed in this paper, which effectively minimizes the noise in CBCT images through utilization of multiple filters and self-attention techniques. Additionally, an Improved U-Net model is proposed, where the original convolution block in the U-Net is replaced with a Double ConvNeXt block to yield better network performance. Experimentally, the proposed Improved U-Net method showed remarkable progress as it achieved a Dice Similarity Coefficient of 86.95% in oral CBCT image segmentation, surpassing existing models and affirming the effectiveness and advancedness of the proposed model and method.

DENTALMODELSEG: FULLY AUTOMATED SEGMENTATION OF UPPER AND LOWER 3D INTRA-ORAL SURFACES.

In this paper, we present a deep learning-based method for surface segmentation. This technique consists of acquiring 2D views and extracting features from the surface such as the normal vectors. The rendered images are analyzed with a 2D convolutional neural network, such as a UNET. We test our method in a dental application for the segmentation of dental crowns. The neural network is trained for multi-class segmentation, using image labels as ground truth. A 5-fold cross-validation was performed, and the segmentation task achieved an average Dice of 0.97, sensitivity of 0.98 and precision of 0.98. Our method and algorithms are available as a 3DSlicer extension.

Deep convolutional neural network for hippocampus segmentation with boundary region refinement.

Accurately segmenting the hippocampus from magnetic resonance (MR) brain images is a crucial step in studying brain disorders. However, this task is challenging due to the low signal contrast of hippocampal images, the irregular shape, and small structural size of the hippocampi. In recent years, several deep convolutional networks have been proposed for hippocampus segmentation, which have achieved state-of-the-art performance. These methods typically use large image patches for training the network, as larger patches are beneficial for capturing long-range contextual information. However, this approach increases the computational burden and overlooks the significance of the boundary region. In this study, we propose a deep learning-based method for hippocampus segmentation with boundary region refinement. Our method involves two main steps. First, we propose a convolutional network that takes large image patches as input for initial segmentation. Then, we extract small image patches around the hippocampal boundary for training the second convolutional neural network, which refines the segmentation in the boundary regions. We validate our proposed method on a publicly available dataset and demonstrate that it significantly improves the performance of convolutional neural networks that use single-size image patches as input. In conclusion, our study proposes a novel method for hippocampus segmentation, which improves upon the current state-of-the-art methods. By incorporating a boundary refinement step, our approach achieves higher accuracy in hippocampus segmentation and may facilitate research on brain disorders.

Longitudinal Variability Analysis on Low-dose Abdominal CT with Deep Learning-based Segmentation.

Metabolic health is increasingly implicated as a risk factor across conditions from cardiology to neurology, and efficiency assessment of body composition is critical to quantitatively characterizing these relationships. 2D low dose single slice computed tomography (CT) provides a high resolution, quantitative tissue map, albeit with a limited field of view. Although numerous potential analyses have been proposed in quantifying image context, there has been no comprehensive study for low-dose single slice CT longitudinal variability with automated segmentation. We studied a total of 1816 slices from 1469 subjects of Baltimore Longitudinal Study on Aging (BLSA) abdominal dataset using supervised deep learning-based segmentation and unsupervised clustering method. 300 out of 1469 subjects that have two year gap in their first two scans were pick out to evaluate longitudinal variability with measurements including intraclass correlation coefficient (ICC) and coefficient of variation (CV) in terms of tissues/organs size and mean intensity. We showed that our segmentation methods are stable in longitudinal settings with Dice ranged from 0.821 to 0.962 for thirteen target abdominal tissues structures. We observed high variability in most organ with ICC<0.5, low variability in the area of muscle, abdominal wall, fat and body mask with average ICC≥0.8. We found that the variability in organ is highly related to the cross-sectional position of the 2D slice. Our efforts pave quantitative exploration and quality control to reduce uncertainties in longitudinal analysis.

An edge-guided method to fruit segmentation in complex environments

Accurate detection and segmentation of fruit is a key factor in the development of smart farming. Problems such as light variation, fruit overlap and leaf shading create a complex environment in orchards and have a significant impact on the development of smart farming. Many current deep learning-based segmentation methods do not make full use of edge information, resulting in inadequate sharpening of the fruit edges obtained from segmentation. To address this problem, an edge-guided based fruit segmentation method (EdgeSegNet) in complex environments is proposed by us. The method first performs feature extraction through the ResNet model as the backbone network, then integrates and refines the high-level semantic and spatial information through the Global Localization Module (GLM) and localizes potential targets in the target region with the help of the proposed Multi-Scale Localization Block (MSLB). Then Boundary Aware Module (BAM) sharpen the edges of potential targets by integrating the feature information of high and low layers, and finally get the accurate segmented image. The principle of the model is blurred positioning, precise sharpening, edge guiding. The experimental results showed that the method achieved an average MIoU of 0.909 and 0.942 on the apple and peach datasets of three different sizes, large, medium and small, respectively, outperforming several other state-of-the-art models in terms of accuracy and complexity as well as inference time.