Ability Of Segments Research Articles

Category-Based Interactive Attention and Perception Fusion Network for Semantic Segmentation of Remote Sensing Images

With the development of CNNs and the application of transformers, the segmentation performance of high-resolution remote sensing image semantic segmentation models has been significantly improved. However, the issue of category imbalance in remote sensing images often leads to the model’s segmentation ability being biased towards categories with more samples, resulting in suboptimal performance for categories with fewer samples. To make the network’s learning and representation capabilities more balanced across different classes, in this paper we propose a category-based interactive attention and perception fusion network (CIAPNet), where the network divides the feature space by category to ensure the fairness of learning and representation for each category. Specifically, the category grouping attention (CGA) module utilizes self-attention to reconstruct the features of each category in a grouped manner, and optimize the foreground–background relationship and its feature representation for each category through the interactive foreground–background relationship optimization (IFBRO) module therein. Additionally, we introduce a detail-aware fusion (DAF) module, which uses shallow detail features to complete the semantic information of deep features. Finally, a multi-scale representation (MSR) module is deployed for each class in the CGA and DAF modules to enhance the description capability of different scale information for each category. Our proposed CIAPNet achieves mIoUs of 54.44%, 85.71%, and 87.88% on the LoveDA urban–rural dataset, and the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam urban datasets, respectively. Compared with current popular methods, our network not only achieves excellent performance but also demonstrates outstanding class balance.

Selective and multi-scale fusion Mamba for medical image segmentation

Given the high variability in the morphology and size of lesion areas in medical images, accurate medical image segmentation requires both precise positioning of global contours and careful processing of local boundaries. This emphasizes the importance of fusing multi-scale local and global features. However, existing CNN and Transformer-based models are often limited by high parameter counts and complex calculations, making it difficult to efficiently integrate these features. To address this challenge, we proposed two innovative optimization architectures: Selective Fusion Mamba (SF-Mamba) and Multi-Scale Fusion Mamba (MF-Mamba). SF-Mamba can flexibly and dynamically adjust the fusion strategy of local and global features according to the characteristics of the lesions, effectively handling segmentation tasks with variable morphology. MF-Mamba enhances the model’s segmentation ability for lesions of different sizes by capturing global information across scales. Based on these two structures, we constructed a lightweight SMM-UNet model, which not only significantly reduces the computational burden (with only 0.038M parameters) but also demonstrates excellent generalization ability and can efficiently adapt to various types of medical images. Extensive tests on the ISIC2017, ISIC2018, and BUSI public datasets show that SMM-UNet achieves excellent segmentation performance with an extremely low parameter cost.

MLFEU-NET: A Multi-scale Low-level Feature Enhancement Unet for breast lesions segmentation in ultrasound images

Breast lesions constantly threaten the health of females. Segmentation methods of breast lesions are important for clinical diagnosis, and neural networks have been widely used and played a significant role in this field. However, false detections and miss detections still exist due to the variability in the shape and location of breast lesions, artifacts and noise in breast ultrasound (BUS) images, and structural defects in conventional segmentation networks. In this paper, a multi-scale low-level feature enhancement Unet (MLFEU-net) structure is presented, consisting of the U-net structure, low-level feature enhancement block (LFEB), and a parallel multiscale feature fusion (PMFF) module. Specifically, LFEB enhances the detail information during shallow downsampling and further feature selection. Meanwhile, in the neck of Unet, PMFF module uses different scales of dilation convolution to provide different sizes of sensory fields, and to efficiently merge shallow and deep features filtered, leads to more accurate segmentation results. To evaluate the MLFEU-net’s segmentation ability, five quantitative metrics were used on two breast ultrasound datasets, and it was compared with several advanced segmentation techniques. The results illustrate that our approach surpasses other methods in performance. Moreover, robustness experiments validate the effectiveness of our approach in achieving robust segmentation of breast lesions.

Unfolding Prosody Guides the Development of Word Segmentation

Prosody is known to scaffold the learning of language, and thus understanding prosodic development is vital for language acquisition. The present study explored the unfolding prosody model of prosodic development (proposed in Frota’s et al. study in 2016) beyond early production data, to examine whether it predicted the development of early segmentation abilities. European Portuguese-learning infants aged between 5 and 17 months were tested in a series of word segmentation experiments. Developing prosodic structure was evidenced in word segmentation as proposed by the unfolding model: (i) a simple monosyllabic word shape crucially placed at a major prosodic edge was segmented first, before more complex word shapes under similar prosodic conditions; (ii) the segmentation of more complex words was easier at a major prosodic edge than in phrase-medial position; and (iii) the segmentation of complex words with an iambic pattern preceded the segmentation of words with a trochaic pattern. These findings demonstrated that word segmentation evolved with unfolding prosody, suggesting that the prosodic units developed in the unfolding process are used both as speech production planning units and to extract word-forms from continuous speech. Therefore, our study contributes to a better understanding of the mechanisms underlying word segmentation, and to a better understanding of early prosodic development, a cornerstone of language acquisition.

Hybrid clinical-radiomics model based on fully automatic segmentation for predicting the early expansion of spontaneous intracerebral hemorrhage: A multi-center study

BackgroundEarly prediction of hematoma expansion (HE) is important for the development of therapeutic strategies for spontaneous intracerebral hemorrhage (sICH). Radiomics can help to predict early hematoma expansion in intracerebral hemorrhage. However, complex image processing procedures, especially hematoma segmentation, are time-consuming and dependent on assessor experience. We provide a fully automated hematoma segmentation method, and construct a hybrid predictive model for risk stratification of hematoma expansion. PurposeTo propose an automatic approach for predicting early hemorrhage expansion after spontaneous intracerebral hemorrhage using deep-learning and radiomics methods. MethodsA total of 258 patients with sICH were retrospectively enrolled for model construction and internal validation, while another two cohorts (n=87, 149) were employed for independent validation. For hemorrhage segmentation, an iterative segmentation procedure was performed to delineate the area using an nnU-Net framework. Radiomics models of intra-hemorrhage and multiscale peri-hemorrhage were established and evaluated, and the best discriminative-scale peri-hemorrhage radiomics model was selected for further analysis. Combining clinical factors and intra- and peri-hemorrhage radiomics signatures, a hybrid nomogram was constructed for the early HE prediction using multivariate logistic regression. For model validation, the receiver operating characteristic (ROC) curve analyses and DeLong test were used to evaluate the performances of the constructed models, and the calibration curve and decision curve analysis were performed for clinical application. ResultsOur iterative auto-segmentation model showed satisfactory results for hematoma segmentation in all four cohorts. The Dice similarity coefficient of this hematoma segmentation model reached 0.90, showing an expert-level accuracy in hematoma segmentation. The consumed time of the efficient delineation was significantly decreased, from 18 min to less than 2 min, with the assistance of the auto-segmentation model. The radiomics model of 2-mm peri-hemorrhage had a preferable area under ROC curve (AUC) of 0.840 (95 % confidence interval [CI]: 0.768, 0.912) compared with the original (0-mm dilatation) model with an AUC of 0.796 (95 % CI: 0.717, 0.875). The clinical–radiomics hybrid model showed better performances for HE prediction, with AUC of 0.853, 0.852, 0.772, and 0.818 in the training, internal validation, and independent validation cohorts 1 and 2, respectively. ConclusionsThe fully automatic clinical–radiomics model based on deep learning and radiomics exhibits a good ability for hematoma segmentation and a favorable performance in stratifying HE risks.

Fusion PCAM R-CNN of Automatic Segmentation for Magnetic Flux Leakage Defects.

Magnetic leakage detection technology plays an important role in the long-oil pipeline. Automatic segmentation of defecting images is crucial for the detection of magnetic flux leakage (MFL) works. At present, accurate segmentation for small defects has always been a difficult problem. In contrast to the state-of-the-art MFL detection methodologies based on convolution neural network (CNN), an optimization method is devised in our study by integrating mask region-based CNN (Mask R-CNN) and information entropy constraint (IEC). To be precise, the principal component analysis (PCA) is utilized to improve the feature learning and network segmentation ability of the convolution kernel. The similarity constraint rule of information entropy is proposed to be inserted into the convolution layer in the Mask R-CNN network. The Mask R-CNN optimizes the convolutional kernel with similar weights or higher similarity, meanwhile, the PCA network reduces the dimension of the feature image to reconstruct the original feature vector. As such, the feature extraction of MFL defects is optimized in the convolution check. The research results can be applied in the field of MFL detection.

A Multi-Level Adaptive Lightweight Net for Damaged Road Marking Detection Based on Knowledge Distillation

To tackle the complexity and limited applicability of high-precision segmentation models for damaged road markings, this study proposes a Multi-level Adaptive Lightweight Network (MALNet) based on knowledge distillation. By incorporating multi-scale dilated convolution and adaptive spatial channel attention fusion modules, the MALNet model significantly enhances the precision, integrity, and robustness of its segmentation branch. Furthermore, it employs an intricate knowledge distillation strategy, channeling rich, layered insights from a teacher model to a student model, thus elevating the latter’s segmentation ability. Concurrently, it streamlines the student model by markedly reducing its parameter count and computational demands, culminating in a segmentation network that is both high-performing and pragmatic. Rigorous testing on three distinct data sets for damaged road marking detection—CDM_P (Collective Damaged road Marking—Public), CDM_H (Collective Damaged road Marking—Highways), and CDM_C (Collective Damaged road Marking—Cityroad)—underscores the MALNet model’s superior segmentation abilities across all damage types, outperforming competing models in accuracy and completeness. Notably, the MALNet model excels in parameter efficiency, computational economy, and throughput. After distillation, the student model’s parameters and computational load decrease to only 31.78% and 27.40% of the teacher model’s, respectively, while processing speeds increase to 1.9 times, demonstrating a significant improvement in lightweight design.

Efficient segmentation of active and inactive plaques in FLAIR-images using DeepLabV3Plus SE with efficientnetb0 backbone in multiple sclerosis

This research paper introduces an efficient approach for the segmentation of active and inactive plaques within Fluid-attenuated inversion recovery (FLAIR) images, employing a convolutional neural network (CNN) model known as DeepLabV3Plus SE with the EfficientNetB0 backbone in Multiple sclerosis (MS), and demonstrates its superior performance compared to other CNN architectures. The study encompasses various critical components, including dataset pre-processing techniques, the utilization of the Squeeze and Excitation Network (SE-Block), and the atrous spatial separable pyramid Block to enhance segmentation capabilities. Detailed descriptions of pre-processing procedures, such as removing the cranial bone segment, image resizing, and normalization, are provided. This study analyzed a cross-sectional cohort of 100 MS patients with active brain plaques, examining 5000 MRI slices. After filtering, 1500 slices were utilized for labeling and deep learning. The training process adopts the dice coefficient as the loss function and utilizes Adam optimization. The study evaluated the model's performance using multiple metrics, including intersection over union (IOU), Dice Score, Precision, Recall, and F1-Score, and offers a comparative analysis with other CNN architectures. Results demonstrate the superior segmentation ability of the proposed model, as evidenced by an IOU of 69.87, Dice Score of 76.24, Precision of 88.89, Recall of 73.52, and F1-Score of 80.47 for the DeepLabV3+SE_EfficientNetB0 model. This research contributes to the advancement of plaque segmentation in FLAIR images and offers a compelling approach with substantial potential for medical image analysis and diagnosis.

BFT‐Net: A transformer‐based boundary feedback network for kidney tumour segmentation

AbstractKidney tumours are among the top ten most common tumours, the automatic segmentation of medical images can help locate tumour locations. However, the segmentation of kidney tumour images still faces several challenges: firstly, there is a lack of renal tumour endoscopic datasets and no segmentation techniques for renal tumour endoscopic images; secondly, the intra‐class inconsistency of tumours caused by variations in size, location, and shape of renal tumours; thirdly, difficulty in semantic fusion during decoding; and finally, the issue of boundary blurring in the localization of lesions. To address the aforementioned issues, a new dataset called Re‐TMRS is proposed, and for this dataset, the transformer‐based boundary feedback network for kidney tumour segmentation (BFT‐Net) is proposed. This network incorporates an adaptive context extract module (ACE) to emphasize local contextual information, reduces the semantic gap through the mixed feature capture module (MFC), and ultimately improves boundary extraction capability through end‐to‐end optimization learning in the boundary assist module (BA). Through numerous experiments, it is demonstrated that the proposed model exhibits excellent segmentation ability and generalization performance. The mDice and mIoU on the Re‐TMRS dataset reach 91.1% and 91.8%, respectively.

ANALYSIS OF TUBERCULOSIS SEGMENTATION ON CHEST X-RAYS USING THE METHOD OF CONVOLUTIONAL NEURAL NETWORK “ATTENTION”

This study presents an experimental study using an advanced Attention U-Net deep learning model to improve tuberculosis (TB) segmentation in chest X-ray images. The goal of this study is to use convolutional neural networks (CNNs) with integrated attentionmechanisms improve the accuracy of medical image analysis. In the experiment, two different datasets were combined and augmented, resulting in a complete set of 800 images. These images were used to thoroughly train and validate the model's performance. The methodical approach allowed the Attention U-Net model to effectively highlight meaningful features while suppressing extraneous background noise. This ability is important for achieving accuracy in medical diagnosis. The empirical results were convincing: the model demonstrated outstanding segmentation abilities, as evidenced by high metrics: accuracy reached 0.9824, Dice score was 0.9767, and IOU was 0.9778. The integration of attention mechanisms into the CNN architecture has improved accuracy, leading to better detection and diagnosis of lung diseases. This study illustrates how the application of advanced machine learning techniques can help radiologists in image analysis.

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.

Radiomics as a measure superior to common similarity metrics for tumor segmentation performance evaluation.

To propose radiomics features as a superior measure for evaluating the segmentation ability of physicians and auto-segmentation tools and to compare its performance with the most commonly used metrics: Dice similarity coefficient (DSC), surface Dice similarity coefficient (sDSC), and Hausdorff distance (HD). The data of 10 lung cancer patients' CT images with nine tumor segmentations per tumor were downloaded from the RIDER (Reference Database to Evaluate Response) database. Radiomics features of 90 segmented tumors were extracted using the PyRadiomics program. The intraclass correlation coefficient (ICC) of radiomics features were used to evaluate the segmentation similarity and compare their performance with DSC, sDSC, and HD. We calculated one ICC per radiomics feature and per tumor for nine segmentations and 36 ICCs per radiomics feature for 36 pairs of nine segmentations. Meanwhile, there were 360 DSC, sDSC, and HD values calculated for 36 pairs for 10 tumors. The ICC of radiomics features exhibited greater sensitivity to segmentation changes than DSC and sDSC. The ICCs of the wavelet-LLL first order Maximum, wavelet-LLL glcm MCC, wavelet-LLL glcm Cluster Shade features ranged from 0.130 to 0.997, 0.033 to 0.978, and 0.160 to 0.998, respectively. On the other hand, all DSC and sDSC were larger than 0.778 and 0.700, respectively, while HD varied from 0 to 1.9mm. The results indicated that the radiomics features could capture subtle variations in tumor segmentation characteristics, which could not be easily detected by DSC and sDSC. This study demonstrates the superiority of radiomics features with ICC as a measure for evaluating a physician's tumor segmentation ability and the performance of auto-segmentation tools. Radiomics features offer a more sensitive and comprehensive evaluation, providing valuable insights into tumor characteristics. Therefore, the new metrics can be used to evaluate new auto-segmentation methods and enhance trainees' segmentation skills in medical training and education.

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.

Infants' abilities to segment word forms from spectrally degraded speech in the first year of life.

Infants begin to segment word forms from fluent speech-a crucial task in lexical processing-between 4 and 7 months of age. Prior work has established that infants rely on a variety of cues available in the speech signal (i.e., prosodic, statistical, acoustic-segmental, and lexical) to accomplish this task. In two experiments with French-learning 6- and 10-month-olds, we use a psychoacoustic approach to examine if and how degradation of the two fundamental acoustic components extracted from speech by the auditory system, namely, temporal (both frequency and amplitude modulation) and spectral information, impact word form segmentation. Infants were familiarized with passages containing target words, in which frequency modulation (FM) information was replaced with pure tones using a vocoder, while amplitude modulation (AM) was preserved in either 8 or 16 spectral bands. Infants were then tested on their recognition of the target versus novel control words. While the 6-month-olds were unable to segment in either condition, the 10-month-olds succeeded, although only in the 16 spectral band condition. These findings suggest that 6-month-olds need FM temporal cues for speech segmentation while 10-month-olds do not, although they need the AM cues to be presented in enough spectral bands (i.e., 16). This developmental change observed in infants' sensitivity to spectrotemporal cues likely results from an increase in the range of available segmentation procedures, and/or shift from a vowel to a consonant bias in lexical processing between the two ages, as vowels are more affected by our acoustic manipulations. RESEARCH HIGHLIGHTS: Although segmenting speech into word forms is crucial for lexical acquisition, the acoustic information that infants' auditory system extracts to process continuous speech remains unknown. We examined infants' sensitivity to spectrotemporal cues in speech segmentation using vocoded speech, and revealed a developmental change between 6 and 10 months of age. We showed that FM information, that is, the fast temporal modulations of speech, is necessary for 6- but not 10-month-old infants to segment word forms. Moreover, reducing the number of spectral bands impacts 10-month-olds' segmentation abilities, who succeed when 16 bands are preserved, but fail with 8 bands.

The effect of the re-segmentation method on improving the performance of rectal cancer image segmentation models.

Rapid and accurate segmentation of tumor regions from rectal cancer images can better understand the patientâs lesions and surrounding tissues, providing more effective auxiliary diagnostic information. However, cutting rectal tumors with deep learning still cannot be compared with manual segmentation, and a major obstacle to cutting rectal tumors with deep learning is the lack of high-quality data sets. We propose to use our Re-segmentation Method to manually correct the model segmentation area and put it into training and training ideas. The data set has been made publicly available. Methods: A total of 354 rectal cancer CT images and 308 rectal region images labeled by experts from Jiangxi Cancer Hospital were included in the data set. Six network architectures are used to train the data set, and the region predicted by the model is manually revised and then put into training to improve the ability of model segmentation and then perform performance measurement. In this study, we use the Resegmentation Method for various popular network architectures. By comparing the evaluation indicators before and after using the Re-segmentation Method, we prove that our proposed Re-segmentation Method can further improve the performance of the rectal cancer image segmentation model.

205 Detection of anemic sheep using ocular conjunctiva images and deep learning technology

Abstract In sheep, severe anemia often results from gastrointestinal nematodes infections, commonly caused by Haemonchus contortus, a blood-sucking nematode. The objective of the study was to develop a model to predict packed cell volume (PCV) in sheep through ocular conjunctiva images as a real-time anemia diagnosis approach. We collected 3,291 ocular conjunctiva images from 392 sheep on three different farms using an iPhone 8 (12-megapixel). To identify the region of interest in the images (ocular conjunctiva), we annotated 480 images using the Segment Anything Model (SAM). Subsequently, we employed an image segmentation algorithm based on U-net, utilizing the original images and annotations obtained from SAM. We then cropped the segmented images to retain only the region containing the ocular conjunctiva. These cropped and segmented images were used as input data, with PCV as the target variable, in both classification and regression models, while exploring three different deep neural network (DNN) architectures: VGG19, Inception v3, and Xception. We set a threshold of 27% (anemic < 27%, non-anemic ≥ 27%) to convert PCV into a binary variable for classification models. The dataset was split in train, validation and test sets using random by sheep strategy to perform the analysis of the segmentation and prediction models. The segmentation was tested using intersection over union (IoU) as an evaluation metric. To assess and compare predictive quality in the test set, we calculated accuracy, precision, recall, and F1 score for classification models, and R2 for regression models. The U-net segmentation model demonstrated good localization accuracy and reliable segmentation ability, with an average IoU of 0.93, 0.84 and 0.68 in the train, validation and test set, respectively. VGG19 outperformed the other models in classifying individuals as anemic or non-anemic, achieving an F1 score of 0.60, indicating its moderate ability to distinguish between these two classes. For regression, Xception provided the best performance with an R2 of 0.29, suggesting that 29% of the variance in PCV can be explained by the model’s predictions. This innovative approach not only expands the possibilities for integrated high-throughput phenotyping through computer vision but also aids in identifying anemic animals. The results suggest that using ocular conjunctiva images and DNN technology can contribute to support farm-level management decisions, and potentially reduce economic losses due to parasitic infections like H. contortus. Furthermore, this approach reduces the cost of blood tests for anemia identification while allowing for real-time anemia diagnosis.

A Point Cloud Segmentation Method for Pigs from Complex Point Cloud Environments Based on the Improved PointNet++

In animal husbandry applications, segmenting live pigs in complex farming environments faces many challenges, such as when pigs lick railings and defecate within the acquisition environment. The pig’s behavior makes point cloud segmentation more complex because dynamic animal behaviors and environmental changes must be considered. This further requires point cloud segmentation algorithms to improve the feature capture capability. In order to tackle the challenges associated with accurately segmenting point cloud data collected in complex real-world scenarios, such as pig occlusion and posture changes, this study utilizes PointNet++. The SoftPool pooling method is employed to implement a PointNet++ model that can achieve accurate point cloud segmentation for live pigs in complex environments. Firstly, the PointNet++ model is modified to make it more suitable for pigs by adjusting its parameters related to feature extraction and sensory fields. Then, the model’s ability to capture the details of point cloud features is further improved by using SoftPool as the point cloud feature pooling method. Finally, registration, filtering, and extraction are used to preprocess the point clouds before integrating them into a dataset for manual annotation. The improved PointNet++ model’s segmentation ability was validated and redefined with the pig point cloud dataset. Through experiments, it was shown that the improved model has better learning ability across 529 pig point cloud data sets. The optimal mean Intersection over Union (mIoU) was recorded at 96.52% and the accuracy at 98.33%. This study has achieved the automatic segmentation of highly overlapping pigs and pen point clouds. This advancement enables future animal husbandry applications, such as estimating body weight and size based on 3D point clouds.

Breast Cancer Tumour Masks Segmentation Using Modified U-Net with Channel Attention

Abstract: A modified U-Net for biomedical image segmentation with channel attention is developed for breast cancer image segmentation. A unique step in biomedical image analysis is segmentation because it determines the accuracy of image analysis algorithms. Algorithms for accurate and early breast cancer detection in women using mammograms depend highly on the accuracy of the segmentation stage. In this thesis, in order to improve on the segmentation ability of the traditional UNet architecture, it was modified by varying the fixed kernel sizes that it was originally designed for. In this model, progressively increasing kernel sizes of 3x3, 5x5, 7x7, 9x9 and 11x11 were used, which is different from the architecture of UNet that depended on fixed 3x3 kernel sizes for all convolutional layers. The first Convolutional block of the proposed model contains two Conv2D layers with 3x3 kernel sizes, ReLU (Rectified Linear Unit) activation function, and 16 channel dimensions. A maxpooling2D layer with pool size of (2x2) was placed to downsample the image size before feeding to the next convolutional block which used the same activation function, with 32 channel dimension and 5x5 kernel size. The next convolutional block used 64 channel dimensions with 7x7 kernel size, then a 9x9 kernel size Conv2D block with 128 channel dimensions was placed before the Conv2D with upsampling. The squeeze and excitation block was placed after this convolutional layer and in-between the two convolutional blocks with 11x11 kernel sizes, to reallocate the weight of the essential features to ensure that more discriminate features are learnt from the breast cancer images to improve the overall representational strength of the network through the performance of dynamic feature recalibration of the image channels. This significantly improved the performance of the segmentation by explicitly modeling the interdependencies between channels in the convolutional layers. The increasing kernel sizes enables the model to learn more discriminate features from the images. Jaccard similarity index, F1 score, Recall, Precision, F2 score, and accuracy results were used to compare the result of the proposed model with other models such as U-Net model and Baseline model. The results shows a better performance of 0.88 for Jaccard similarity index, 0.94 for F1 score, 0.93 for recall, 0.92 for precision, 0.92 for F2 score, and an accuracy score of 0.98%. The model is good for biomedical image segmentation.

Diabetic Retinopathy Lesion Segmentation Method Based on Multi-Scale Attention and Lesion Perception

The early diagnosis of diabetic retinopathy (DR) can effectively prevent irreversible vision loss and assist ophthalmologists in providing timely and accurate treatment plans. However, the existing methods based on deep learning have a weak perception ability of different scale information in retinal fundus images, and the segmentation capability of subtle lesions is also insufficient. This paper aims to address these issues and proposes MLNet for DR lesion segmentation, which mainly consists of the Multi-Scale Attention Block (MSAB) and the Lesion Perception Block (LPB). The MSAB is designed to capture multi-scale lesion features in fundus images, while the LPB perceives subtle lesions in depth. In addition, a novel loss function with tailored lesion weight is designed to reduce the influence of imbalanced datasets on the algorithm. The performance comparison between MLNet and other state-of-the-art methods is carried out in the DDR dataset and DIARETDB1 dataset, and MLNet achieves the best results of 51.81% mAUPR, 49.85% mDice, and 37.19% mIoU in the DDR dataset, and 67.16% mAUPR and 61.82% mDice in the DIARETDB1 dataset. The generalization experiment of MLNet in the IDRiD dataset achieves 59.54% mAUPR, which is the best among other methods. The results show that MLNet has outstanding DR lesion segmentation ability.

A serial semantic segmentation model based on encoder-decoder architecture

The thriving progress of Convolutional Neural Networks (CNNs) and the outstanding efficacy of Visual Transformers (ViTs) have delivered impressive outcomes in the domain of semantic segmentation. However, each model in isolation entails a trade-off between high computational complexity and compromised computational efficiency. To address this challenge, we effectively combine the CNN and encoder-decoder structures in a Transformer-inspired fashion, presenting the Serial Semantic Segmentation Trans via CNN Former (SSS-Former) model. To augment the feature extraction capability, we utilize the meticulously crafted SSS-CSPNet, resulting in a well-designed architecture for the holistic model. We propose a novel SSS-PN attention network that enhances the spatial topological connections of features, leading to improved overall performance. Additionally, the integration of SASPP bridges the semantic gap between multi-scale features and enhances segmentation ability for overlapping objects. To fulfill the requirement of real-time segmentation, we leverage a novel restructuring technique to devise a more lightweight and faster ResSSS-Former model. Abundant experimental results demonstrate that both SSS-Former and ResSSS-Former outperform existing state-of-the-art methods in terms of computational efficiency, result precision, and speed. Remarkably, SSS-Former achieves a mIoU of 58.63 % at 89.1FPS on the ADE20K dataset. On the validation and testing datasets of CityScapes, it obtains mIoU scores of 85.1 % and 85.2 % respectively, with a speed of 94.1FPS. Our optimized ResSSS-Former achieves impressive real-time segmentation results, with an astonishing 100+FPS while maintaining high segmentation accuracy. The compelling results from the ISPRS datasets further validate the effectiveness of our proposed models in segmenting multi-scale and overlapping objects.