Existing Segmentation Methods Research Articles

ELA-Net: An Efficient Lightweight Attention Network for Skin Lesion Segmentation.

In clinical conditions limited by equipment, attaining lightweight skin lesion segmentation is pivotal as it facilitates the integration of the model into diverse medical devices, thereby enhancing operational efficiency. However, the lightweight design of the model may face accuracy degradation, especially when dealing with complex images such as skin lesion images with irregular regions, blurred boundaries, and oversized boundaries. To address these challenges, we propose an efficient lightweight attention network (ELANet) for the skin lesion segmentation task. In ELANet, two different attention mechanisms of the bilateral residual module (BRM) can achieve complementary information, which enhances the sensitivity to features in spatial and channel dimensions, respectively, and then multiple BRMs are stacked for efficient feature extraction of the input information. In addition, the network acquires global information and improves segmentation accuracy by putting feature maps of different scales through multi-scale attention fusion (MAF) operations. Finally, we evaluate the performance of ELANet on three publicly available datasets, ISIC2016, ISIC2017, and ISIC2018, and the experimental results show that our algorithm can achieve 89.87%, 81.85%, and 82.87% of the mIoU on the three datasets with a parametric of 0.459 M, which is an excellent balance between accuracy and lightness and is superior to many existing segmentation methods.

Attention decoupled contrastive learning for semi-supervised segmentation method based on data augmentation

Objective. Deep learning algorithms have demonstrated impressive performance by leveraging large labeled data. However, acquiring pixel-level annotations for medical image analysis, especially in segmentation tasks, is both costly and time-consuming, posing challenges for supervised learning techniques. Existing semi-supervised methods tend to underutilize representations of unlabeled data and handle labeled and unlabeled data separately, neglecting their interdependencies. Approach. To address this issue, we introduce the Data-Augmented Attention-Decoupled Contrastive model (DADC). This model incorporates an attention decoupling module and utilizes contrastive learning to effectively distinguish foreground and background, significantly improving segmentation accuracy. Our approach integrates an augmentation technique that merges information from both labeled and unlabeled data, notably boosting network performance, especially in scenarios with limited labeled data. Main results. We conducted comprehensive experiments on the automated breast ultrasound (ABUS) dataset and the results demonstrate that DADC outperforms existing segmentation methods in terms of segmentation performance.

Lightweight multi-scale dynamic selection network for medical image segmentation

Medical image segmentation technology can assist doctors in accurately diagnosing and treating diseases. However, the diversity of segmented targets in scale and shape, as well as the complexity of the background environment, pose challenges for existing segmentation methods. In this context, we propose two effective deep neural networks, the multi-scale dynamic selection network (MDSNet) and its lightweight version MDSNet-Light for medical image segmentation. In MDSNet, we design a multi-scale dynamic selection module to capture multi-scale contextual information and dynamically adjust the acceptance domain of the feature map to achieve feature fusion at the optimal scale. We introduce PixelShuffle and PixelUnshuffle as sampling methods to effectively alleviate information loss issues for small-scale objects caused by pooling. Experimental results show that MDSNet outperforms eight existing superior neural networks in the test data of the publicly available dataset LIDC-IDRI on four evaluation indicators. And MDSNet-Light achieves a good balance between network performance and computational complexity, providing an option for applications in limited computing resource scenarios.

Automated segmentation of liver tumors from computed tomographic scans

The precision of liver tumor segmentation heavily depends on the doctor's expertise, hence it is required to produce an algorithm for automatic liver tumor segmentation to reduce the manual intervention in assessing liver disease identification. We propose a CNN-based UNet architecture designed to segment liver tumors from CT images of size 128×128. In this model, modifications were made to the encoder, decoder, and bridge paths to enhance feature extraction efficiency. The performance of the modified UNet was evaluated against an existing segmentation method using the same CT image size. The comparison focused on the Dice similarity coefficient and accuracy. Our proposed method demonstrated a high Dice similarity coefficient of 75.37 % and an accuracy of 99.75 % on the 3Dircadb dataset. These results indicate that our modified UNet achieved superior segmentation metrics compared to state-of-the-art methods, showcasing its effectiveness in liver tumor segmentation.

From pixels to pathology: A novel dual-pathway multi-scale hierarchical upsampling network for MRI-based prostate zonal segmentation

Prostate cancer is a prevalent and life-threatening disease characterized by abnormal cell growth within the prostate gland. Early and accurate diagnosis of prostate cancer is crucial for effective treatment planning. Magnetic Resonance Imaging (MRI) is valuable for diagnosing and assessing prostate cancer. Medical professionals use MRI to create segmentation for detecting prostate cancer. However, existing segmentation methods are limited in accurately delineating anatomical structures and tumor regions within the prostate. This research proposes an innovative methodology for advancing MRI-based prostate segmentation. The objective is to encompass anatomical and tumor zones within the prostate, facilitating precise diagnosis and treatment planning. The proposed dual-pathway multi-scale hierarchical upsampling network introduces significant modifications compared to the traditional UNet-based architecture. It outperforms previous studies, demonstrating superior performance in anatomical segmentation on both the ProstateX and Prostate158 datasets. It achieves an intersection over union of 0.8449 and a dice similarity coefficient of 0.9872 on ProstateX, as well as an intersection over union of 0.8065 and a dice similarity coefficient of 0.9831 on Prostate158, suppressing previous research by a significant margin. These results highlight the potential of this approach to advance the utility of MRI in diagnosing and planning the treatment of prostate-related pathologies, benefiting both patients and healthcare practitioners.

Brain tumor segmentation with missing MRI modalities using edge aware discriminative feature fusion based transformer U-net

Brain tumor segmentation is an essential task for medical diagnosis and treatment planning. Multi-modal MRI provides complementary information that is essential for accurate segmentation of brain tumors, but missing modality images are a common problem in clinical practice. Existing segmentation methods often fail to generate accurate object boundaries and selectively fuse the tumor region, resulting in unreliable segmentation masks. In this work, we propose an Edge-aware Discriminative Feature Fusion Based Transformer U-Net (EA-DFFTU-Net) to segment the brain tumor effectively even in the absence of modalities. First, the MRI input data is pre-processed, and features are then extracted using a ResNet-50 encoder, which learns local spatial. We employ an Edge Feature Module (EFM) to acquire edge attention representations. These features are then transferred to the Discriminative Feature Fusion based Transformer U-Net (DFFTU-Net) that learns global contexts and captures multiscale features. We use a Discriminative Feature Fusion Module (DFFM) in the decoder of the DFFTU-Net to effectively fuse the multiscale features in order to obtain accurate segmentation. The performance of the proposed EA-DFFTU-Net segmentation method was determined by evaluating it and comparing the obtained results with those of existing brain tumor segmentation techniques.

Novel deep learning methods for 3D flow field segmentation and classification

Flow field segmentation and classification help researchers to understand vortex structure and thus turbulent flow. Existing deep learning methods are mainly based on global vorticity information and focused on 2D circumstance. In this paper, based on flow field theory, criteria for flow field segmentation and classification in 3D space using local velocity information and the physical relationship between local vorticity and vortex wake are conducted, and then novel physical local information-embedded flow field segmentation and classification methods in 3D space using Multilayer Perceptron (MLP) are proposed. Our methods consist of three modules: a preprocessing module, a flow field segmentation module and a flow field classification module. The preprocessing module calculates input for succeeding modules based on the above criteria. The segmentation and classification modules process corresponding input to identify vortex structure, and further classify the type of vortex wakes in a 3D flow field. Experimental results show that our method can identify vortex area with an accuracy rate that reaches 90% while reducing time consumption by more than 90% compared with existing segmentation methods; our classification method reaches an accuracy rate of 99.7% while reducing time consumption by more than 90% compared with existing classification methods. Our proposed methods proved to achieve good performance both on accuracy and efficiency.

Glioma segmentation based on dense contrastive learning and multimodal features recalibration

Accurate segmentation of different regions of gliomas from multimodal magnetic resonance (MR) images is crucial for glioma grading and precise diagnosis, but many existing segmentation methods are difficult to effectively utilize multimodal MR image information to recognize accurately the lesion regions with small size, low contrast and irregular shape. To address this issue, this work proposes a novel 3D glioma segmentation model DCL-MANet. DCL-MANet has an architecture of multiple encoders and one single decoder. Each encoder is used to extract MR image features of a given modality. To overcome the entangle problems of multimodal semantic features, a dense contrastive learning (DCL) strategy is presented to extract the modality-specific and common features. Following that, feature recalibration block (RFB) based on modality-wise attention is used to recalibrate the semantic features of each modality, enabling the model to focus on the features that are beneficial for glioma segmentation. These recalibrated features are input into the decoder to obtain the segmentation results. To verify the superiority of the proposed method, we compare it with several state-of-the-art (SOTA) methods in terms of Dice, average symmetric surface distance (ASSD), HD95 and volumetric similarity (Vs). The comparison results show that the average Dice, ASSD, HD95 and Vs of DCL-MANet on all tumor regions are improved at least by 0.66%, 3.47%, 8.94% and 1.07% respectively. For small enhance tumor (ET) region, the corresponding improvement can be up to 0.37%, 7.83%, 11.32%, and 1.35%, respectively. In addition, the ablation results demonstrate the effectiveness of the proposed DCL and RFB, and combining them can significantly increase Dice (1.59%) and Vs (1.54%) while decreasing ASSD (40.51%) and HD95 (45.16%) on ET region. The proposed DCL-MANet could disentangle multimodal features and enhance the semantics of modality-dependent features, providing a potential means to accurately segment small lesion regions in gliomas.

Neuron image segmentation based on convolution and BN fusion and multi-input feature fusion

The segmentation of neuronal morphology in electron microscopy images is crucial for the analysis and understanding of neuronal function. However, most of the existing segmentation methods are not suitable for challenging datasets where the neuronal structure is contaminated by noise or has interrupted parts. In this paper, we propose a segmentation method based on deep learning to determine the location information of neurons and reduce the influence of image noise in the data. Specifically, we adapt our neuron dataset based on UNet by using convolution with BN fusion and multi-input feature fusion. The method is named REDAFNet. The model simplifies the model structure and enhances the generalization ability by fusing the convolution layer and BN layer. The noise interference in the data was reduced by multi-input feature fusion, and the ability to understand and express the data was enhanced. The method takes a neuron image as input and its pixel segmentation map as output. Experimental results show that the segmentation accuracy of the proposed method is 91.96%, 93.86% and 80.25% on the ISBI2012 dataset, U-RISC retinal neuron dataset and N2DH-GOWT1 stem cell dataset, respectively. Compared with the existing segmentation methods, the proposed method can extract more complete feature information and achieve more accurate segmentation.

A framework for identification of brain tumors from MR images using progressive segmentation

PurposeThis study addresses the critical health issue of brain tumors, focusing on enhancing the accuracy of tumor segmentation from Magnetic Resonance Imaging (MRI) images. The primary research question investigates the effectiveness of a novel Hybrid Watershed–Clustering framework and its underlying Progressive Segmentation of the MR Images using the Radius and Intensity Measure (PS-RIM) algorithm. The aim is to improve the detection and segmentation of brain tumors within MR images, surpassing the efficacy of current methodologies.MethodsThe methodology involves a three-stage process. In the preprocessing stage, noise reduction and intensity normalization techniques are applied to clarify the images. The next stage is region-based segmentation, which includes morphological processing, edge detection, and thresholding to delineate tumor areas accurately. The final post-processing stage enhances segmentation accuracy and reduces false positives by integrating clustering machine learning techniques, specifically the K-Means cluster algorithm, to refine tumor identification.ResultsThe framework's comprehensive evaluation across various MR images shows a significant improvement in accuracy over existing segmentation methods. The PS-RIM algorithm within the framework effectively captures the diverse presentations of tumor appearances in MR images. The research recorded an impressive accuracy rate of 98.11% in tumor detection, demonstrating enhanced identification and segmentation quality.ConclusionsThe study concludes that the proposed Hybrid Watershed–Clustering framework, powered by the PS-RIM algorithm, markedly improves the detection and differentiation of brain tumors in MR images. It exhibits exceptional accuracy, resilience, and computational efficiency. These findings hold substantial potential for advancing computer vision and image analysis in medical diagnostics, which could improve patient outcomes in managing brain tumors.Graphical abstract

An Automated Multi-scale Feature Fusion Network for Spine Fracture Segmentation Using Computed Tomography Images.

Spine fractures represent a critical health concern with far-reaching implications for patient care and clinical decision-making. Accurate segmentation of spine fractures from medical images is a crucial task due to its location, shape, type, and severity. Addressing these challenges often requires the use of advanced machine learning and deep learning techniques. In this research, a novel multi-scale feature fusion deep learning model is proposed for the automated spine fracture segmentation using Computed Tomography (CT) to these challenges. The proposed model consists of six modules; Feature Fusion Module (FFM), Squeeze and Excitation (SEM), Atrous Spatial Pyramid Pooling (ASPP), Residual Convolution Block Attention Module (RCBAM), Residual Border Refinement Attention Block (RBRAB), and Local Position Residual Attention Block (LPRAB). These modules are used to apply multi-scale feature fusion, spatial feature extraction, channel-wise feature improvement, segmentation border results border refinement, and positional focus on the region of interest. After that, a decoder network is used to predict the fractured spine. The experimental results show that the proposed approach achieves better accuracy results in solving the above challenges and also performs well compared to the existing segmentation methods.

EpiSegMix: a flexible distribution hidden Markov model with duration modeling for chromatin state discovery.

Automated chromatin segmentation based on ChIP-seq (chromatin immunoprecipitation followed by sequencing) data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality. We introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths. In a comparison with existing tools, ChromHMM, Segway, and EpiCSeg, we show that EpiSegMix is more predictive of cell biology, such as gene expression. Its flexible framework enables it to fit an accurate probabilistic model, which has the potential to increase the biological interpretability of chromatin states. Source code: https://gitlab.com/rahmannlab/episegmix.

NaMa: Neighbor-Aware Multi-Modal Adaptive Learning for Prostate Tumor Segmentation on Anisotropic MR Images

Accurate segmentation of prostate tumors from multi-modal magnetic resonance (MR) images is crucial for diagnosis and treatment of prostate cancer. However, the robustness of existing segmentation methods is limited, mainly because these methods 1) fail to adaptively assess subject-specific information of each MR modality for accurate tumor delineation, and 2) lack effective utilization of inter-slice information across thick slices in MR images to segment tumor as a whole 3D volume. In this work, we propose a two-stage neighbor-aware multi-modal adaptive learning network (NaMa) for accurate prostate tumor segmentation from multi-modal anisotropic MR images. In particular, in the first stage, we apply subject-specific multi-modal fusion in each slice by developing a novel modality-informativeness adaptive learning (MIAL) module for selecting and adaptively fusing informative representation of each modality based on inter-modality correlations. In the second stage, we exploit inter-slice feature correlations to derive volumetric tumor segmentation. Specifically, we first use a Unet variant with sequence layers to coarsely capture slice relationship at a global scale, and further generate an activation map for each slice. Then, we introduce an activation mapping guidance (AMG) module to refine slice-wise representation (via information from adjacent slices) for consistent tumor segmentation across neighboring slices. Besides, during the network training, we further apply a random mask strategy to each MR modality to improve feature representation efficiency. Experiments on both in-house and public (PICAI) multi-modal prostate tumor datasets show that our proposed NaMa performs better than state-of-the-art methods.

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

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

HRD-Net: High resolution segmentation network with adaptive learning ability of retinal vessel features

Retinal segmentation is a crucial step in the early warning of human health conditions. However, retinal blood vessels possess complex curvature, irregular distribution, and contain multi-scale fine structures, which make the limited receptive field of regular convolution challenging to process their vascular details efficiently. Additionally, the encoder-decoder based network leads to irreversible spatial information loss because of multiple downsampling, resulting in over-segmentation and missed segmentation of the vessels. For this reason, we develop a high-resolution network based on Deformable Convolution v3, called HRD-Net. By constructing a high-resolution representation, the network allows special attention to be paid to the details of tiny blood vessels. The proposed feature enhancement cascade module based on Deformable Convolution v3 can flexibly adapt and capture the ever-changing morphology and intricate connections of retinal blood vessels, ensuring the continuity of vessel segmentation. In the output phase of the network, the proposed global aggregation module integrates full-resolution feature maps while suppressing redundant features, achieving an effective fusion of high-level semantic information and spatial detail information. In addition, we have re-examined the selection criteria for activation and normalization methods, and also refine the network architectures from a spatial domain perspective to release redundant computational loads. Testing on the DRIVE, STARE, and CHASE_DB1 datasets indicates that HRD-Net, with fewer parameters, outperforms existing segmentation methods on several evaluation metrics such as F1, ACC, SE, SP, AUC, and IOU.

STCNet: Alternating CNN and improved transformer network for COVID-19 CT image segmentation

Since the emergence of the Corona Virus Disease in 2019 (COVID-19), it has become a serious health problem affecting the human respiratory system. At present, automatic segmentation of lung infection areas from Computed Tomography has been playing a crucial role in the diagnosis of this disease because of its ability to perform pathological studies based on the lung infection areas. However, due to the lung infection areas scattered distribution, the existing segmentation methods generally have the problems of missing and incomplete segmentation. The Convolutional Neural Network (CNN)-based approaches generally lack the ability to model explicit long-range relation, and the transformer-based methods are not conducive to capturing the detailed boundaries of infected areas. Whereas the infected regions of the coronavirus images are scattered and boundary information plays an important role, both the boundaries and the global infected areas need to be taken into account. Therefore, we propose a novel coronavirus image segmentation network alternately using Swin transformer and CNN (STCNet). Firstly, to enable network to capture richer features, the ReSwin transformer block is proposed and added after each level of convolution block in the encoder-decoder. Secondly, to effectively retain the infected areas boundary information, the skip connection cross attention module is used to provide spatial information to each decoder. And through the fine-tuned scale-aware pyramid fusion module to fuse multi-scale context information. Experimental results show that STCNet at can achieve state-of-the-art performance on two coronavirus segmentation datasets, with Dice achieves 79.92 % and 82.78 %, respectively. Our code is available at https://github.com/sineagles/STCNet.

FE-Net: Feature enhancement segmentation network

Semantic segmentation is one of the directions in image research. It aims to obtain the contours of objects of interest, facilitating subsequent engineering tasks such as measurement and feature selection. However, existing segmentation methods still lack precision in class edge, particularly in multi-class mixed region. To this end, we present the Feature Enhancement Network (FE-Net), a novel approach that leverages edge label and pixel-wise weights to enhance segmentation performance in complex backgrounds. Firstly, we propose a Smart Edge Head (SE-Head) to process shallow-level information from the backbone network. It is combined with the FCN-Head and SepASPP-Head, located at deeper layers, to form a transitional structure where the loss weights gradually transition from edge labels to semantic labels and a mixed loss is also designed to support this structure. Additionally, we propose a pixel-wise weight evaluation method, a pixel-wise weight block, and a feature enhancement loss to improve training effectiveness in multi-class regions. FE-Net achieves significant performance improvements over baselines on publicly datasets Pascal VOC2012, SBD, and ATR, with best mIoU enhancements of 15.19%, 1.42% and 3.51%, respectively. Furthermore, experiments conducted on Pole&Hole match dataset from our laboratory environment demonstrate the superior effectiveness of FE-Net in segmenting defined key pixels.

BLADDER WALL SEGMENTATION AND CANCER ASSESSMENT USING ATTENTION U-NET WITH ADVERSARIAL MECHANISM AND TWO-LAYER LEVEL SETS

Improving the segmentation of bladder mucosa and serosa is a significant advancement for enhancing the diagnosis and treatment of bladder cancer, leading to a better understanding of the disease and improved decision-making accuracy for physicians. However, the primary aim of multi-region segmentation in bladder cancer is to facilitate the staging of the disease, specifically distinguishing between muscle-invasive bladder cancer and nonmuscle-invasive bladder cancer. This differentiation is crucial as urothelial cancers become increasingly aggressive as they infiltrate and penetrate the bladder wall. Yet, none of the existing segmentation methods have effectively addressed this critical issue. In our study, an attention U-Net with an adversarial mechanism is proposed, combining with two-layer level sets, to address these challenges. The attention U-Net with an adversarial mechanism captures the inner and outer boundaries of the bladder, including the tumor. Moreover, a two-layer level sets is proposed to further catching subpixels of the bladder wall and gets results with Dice score of 0.91. By calculating the convexity of the two-level sets, the two types of tumor are successfully classified, achieving an experimental accuracy of 0.88. This innovative approach significantly contributes to the field of bladder cancer segmentation and classification, providing valuable insights for improved diagnosis and treatment strategies.

Automated wound care by employing a reliable U-Net architecture combined with ResNet feature encoders for monitoring chronic wounds.

Quality of life is greatly affected by chronic wounds. It requires more intensive care than acute wounds. Schedule follow-up appointments with their doctor to track healing. Good wound treatment promotes healing and fewer problems. Wound care requires precise and reliable wound measurement to optimize patient treatment and outcomes according to evidence-based best practices. Images are used to objectively assess wound state by quantifying key healing parameters. Nevertheless, the robust segmentation of wound images is complex because of the high diversity of wound types and imaging conditions. This study proposes and evaluates a novel hybrid model developed for wound segmentation in medical images. The model combines advanced deep learning techniques with traditional image processing methods to improve the accuracy and reliability of wound segmentation. The main objective is to overcome the limitations of existing segmentation methods (UNet) by leveraging the combined advantages of both paradigms. In our investigation, we introduced a hybrid model architecture, wherein a ResNet34 is utilized as the encoder, and a UNet is employed as the decoder. The combination of ResNet34's deep representation learning and UNet's efficient feature extraction yields notable benefits. The architectural design successfully integrated high-level and low-level features, enabling the generation of segmentation maps with high precision and accuracy. Following the implementation of our model to the actual data, we were able to determine the following values for the Intersection over Union (IOU), Dice score, and accuracy: 0.973, 0.986, and 0.9736, respectively. According to the achieved results, the proposed method is more precise and accurate than the current state-of-the-art.

MSFF: An automated multi-scale feature fusion deep learning model for spine fracture segmentation using MRI

Spinal fractures pose a significant healthcare challenge, impacting patient care and clinical decisions. Accurately identifying these fractures from medical images is crucial due to their location, shape, type, and severity. Meeting these challenges often necessitates employing advanced machine learning and deep learning techniques. This study introduces a novel deep learning model called MSFF (Multi-Scale Feature Fusion) specifically designed for automated spine fracture segmentation using MRI images. The MSFF architecture comprises five key modules: the Feature Fusion Module (FFM), Squeeze and Excitation (SEM), Atrous Spatial Pyramid Pooling (ASPP), Residual Convolution Block Attention Module (RCBAM), Residual Border Refinement Attention Block (RBRAB), and Local Position Residual Attention Block (LPRAB). These modules collectively facilitate multi-scale feature fusion, spatial feature extraction, channel-wise feature enhancement, segmentation border refinement, and focused attention on the region of interest. Subsequently, a decoder network is employed to predict fractured spine areas. Experimental findings demonstrate that the proposed MSFF approach attains superior accuracy in addressing the challenges and outperforms existing segmentation methods.