Medical Imaging Research Articles

Medical Image Fusion for Multiple Diseases Features Enhancement

ABSTRACTThroughout the past 20 years, medical imaging has found extensive application in clinical diagnosis. Doctors may find it difficult to diagnose diseases using only one imaging modality. The main objective of multimodal medical image fusion (MMIF) is to improve both the accuracy and quality of clinical assessments by extracting structural and spectral information from source images. This study proposes a novel MMIF method to assist doctors and postoperations such as image segmentation, classification, and further surgical procedures. Initially, the intensity‐hue‐saturation (IHS) model is utilized to decompose the positron emission tomography (PET)/single photon emission computed tomography (SPECT) image, followed by a hue‐angle mapping method to discriminate high‐ and low‐activity regions in the PET images. Then, a proposed structure feature adjustment (SFA) mechanism is used as a fusion strategy for high‐ and low‐activity regions to obtain structural and anatomical details with minimum color distortion. In the second step, a new multi‐discriminator generative adversarial network (MDcGAN) approach is proposed for obtaining the final fused image. The qualitative and quantitative results demonstrate that the proposed method is superior to existing MMIF methods in preserving the structural, anatomical, and functional details of the PET/SPECT images. Through our assessment, involving visual analysis and subsequent verification using statistical metrics, it becomes evident that color changes contribute substantial visual information to the fusion of PET and MR images. The quantitative outcomes demonstrate that, in the majority of cases, the proposed algorithm consistently outperformed other methods. Yet, in a few instances, it achieved the second‐highest results. The validity of the proposed method was confirmed using diverse modalities, encompassing a total of 1012 image pairs.

Wide FOV metalens for near-infrared capsule endoscopy: advancing compact medical imaging

Abstract This study presents the design, fabrication, and characterization of a wide field-of-view (FOV) metalens optimized for capsule endoscopy. The metalens achieved a 165° FOV with a high modulation transfer function (MTF) of 300 lines per millimeter (lp/mm) across the entire FOV, operating in the near-infrared (NIR) narrow-bandpass imaging at 940 nm. The performance of the metalens-based system is evaluated using two bandwidths, 12 nm and 32 nm, showing MTF values of 0.2 and 0.3 at 250 lp/mm, respectively. The metalens-based system maintains a compact form factor with a total track length of 1.4 mm and a diameter of 1.58 mm. Compared to a traditional 108° FOV endoscope, the nano-optic capsule endoscope demonstrated superior performance in terms of FOV, contrast, and resolution. This advancement represents a significant step toward enhancing diagnostic capabilities in medical imaging, offering improved performance in a more compact package compared to conventional optics.

Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis.

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically 18F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges. Over the past few years, radiomics and artificial intelligence (AI) have surfaced as valuable tools for detecting subtle features within medical images that may not be easily discerned by visual assessment. The rapid expansion of AI and its application in medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomics and AI capabilities seem to hold promise across various clinical scenarios related to lymphoma. Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliability and standardize their applications. This review aims to provide a comprehensive perspective on the current literature regarding the application of AI and radiomics applied/extracted on/from 18F-FDG PET/CT in the management of lymphoma patients.

Multi-physics simulations reveal hemodynamic impacts of patient-derived fibrosis-related changes in left atrial tissue mechanics.

Left atrial (LA) fibrosis is associated with arrhythmogenesis and increased risk of ischemic stroke; its extent and pattern can be quantified on a patient-specific basis using late gadolinium enhancement magnetic resonance imaging.Current stroke risk prediction tools have limited personalization, and their accuracy could be improved by incorporating patient-specific information like fibrotic maps and hemodynamic patterns.We present the first electro-mechano-fluidic multi-physics computational simulations of LA flow, including fibrosis and anatomies from medical imaging. Mechanical changes in fibrotic tissue impair global LA motion, decreasing LA and left atrial appendage (LAA) emptying fractions, especially in subjects with higher fibrosis burdens. Fibrotic-mediated LA motion impairment alters LA and LAA flow near the endocardium and the whole cavity, ultimately leading to more stagnant blood regions in the LAA.

Dopaminergic- and Serotonergic-Dependent Behaviors Are Altered by Lanthanide Series Metals in Caenorhabditis elegans.

The lanthanide series elements are transition metals used as critical components of electronics, as well as rechargeable batteries, fertilizers, antimicrobials, contrast agents for medical imaging, and diesel fuel additives. With the surge in their utilization, lanthanide metals are being found more in our environment. However, little is known about the health effects associated with lanthanide exposure. Epidemiological studies as well as studies performed in rodents exposed to lanthanum (La) suggest neurological damage, learning and memory impairment, and disruption of neurotransmitter signaling, particularly in serotonin and dopamine pathways. Unfortunately, little is known about the neurological effects of heavier lanthanides. As dysfunctions of serotonergic and dopaminergic signaling are implicated in multiple neurological conditions, including Parkinson's disease, depression, generalized anxiety disorder, and post-traumatic stress disorder, it is of utmost importance to determine the effects of La and other lanthanides on these neurotransmitter systems. We therefore hypothesized that early-life exposure of light [La (III) or cerium (Ce (III))] or heavy [erbium (Er (III)) or ytterbium (Yb (III))] lanthanides in Caenorhabditis elegans could cause dysregulation of serotonergic and dopaminergic signaling upon adulthood. Serotonergic signaling was assessed by measuring pharyngeal pump rate, crawl-to-swim transition, as well as egg-laying behaviors. Dopaminergic signaling was assessed by measuring locomotor rate and egg-laying and swim-to-crawl transition behaviors. Treatment with La (III), Ce (III), Er (III), or Yb (III) caused deficits in serotonergic or dopaminergic signaling in all assays, suggesting both the heavy and light lanthanides disrupt these neurotransmitter systems. Concomitant with dysregulation of neurotransmission, all four lanthanides increased reactive oxygen species (ROS) generation and decreased glutathione and ATP levels. This suggests increased oxidative stress, which is a known modifier of neurotransmission. Altogether, our data suggest that both heavy and light lanthanide series elements disrupt serotonergic and dopaminergic signaling and may affect the development or pharmacological management of related neurological conditions.

Neuroimaging-Based Brain Morphometry in Alzheimer’s Disease

Background/Objectives: Alzheimer’s disease (AD) is a leading cause of death worldwide, affecting millions of older Americans and resulting in a substantial economic burden. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) aims to investigate and develop treatments for AD. Methods: This study included 60 participants, divided equally into AD and control cohorts, and utilized magnetic resonance imaging (MRI) scans to detect gray matter volumetric alterations, a key biomarker of AD. The participants’ cortical volume and surface area were quantified using an automated pipeline in MIMICS (Materialise Interactive Medical Imaging Control System). Results: A multivariate regression analysis was conducted to explore the relationship between cortical measurements and potential factors influencing AD susceptibility. The study found that both cortical volume and surface area were statistically significant predictors of AD (p = 0.0004 and p = 0.011, respectively). Age was also a significant factor, with the 65–70 age group showing the strongest association (p < 0.001). The model achieved an accuracy of 0.68 in predicting AD. Conclusions: While voxel-based morphometry (VBM) using MIMICS showed promise, further development of the automated pipeline could enhance accuracy and correlation indices. These findings contribute to our understanding of brain atrophy in AD pathophysiology and highlight the potential of MRI morphometry as a tool for AD biomarker development.

A robust dual-layer medical image watermarking scheme based on matrix factorization in the LWT domain for E-healthcare applications

A robust dual-layer medical image watermarking scheme based on matrix factorization in the LWT domain for E-healthcare applications

Electrode-Dependent and Tunable Sub-to-Super-Linear Responsivity in Mott Material-Enabled Near-Infrared Photodetectors for Advanced Near-Sensor Image Processing.

Brain-like intelligence is ushering humanity into an era of the Internet of Perceptions (IoP), where the vast amounts of data generated by numerous sensing nodes pose significant challenges to transmission bandwidth and computing hardware. A recently proposed near-sensor computing architecture offers an effective solution to reduce data processing delays and energy consumption. However, a pressing need remains for innovative hardware with multifunctional near-sensor image processing capabilities. In this work, Mott material (vanadium dioxide)-based photothermoelectric near-infrared photodetectors are developed that exhibit electrode-dependent and tunable super-linear photoresponse (exponent α > 33) with ultralow modulation bias. These devices demonstrate an opto-thermo-electro-coupled phase transition, resulting in a large photocurrent on/off ratio (>105), high responsivity (≈500 A W-1), and well detectivity (≈3.9× 1012 Jones), all while maintaining rapid response speeds (τr = 2 µs and τd = 5 µs) under the bias of 1V. This electrode-dependent super-linear response is found to arise from the electron doping effect determined by the polarity of the Seebeck coefficient. Furthermore, the work showcases intensity-selective near-sensor processing and night vision pattern reorganization, even with noisy inputs. This work paves the way for developing near-sensor devices with potential applications in medical image preprocessing, flexible electronics, and intelligent edge sensing.

Plant‐Based Biosynthesis of Metal and Metal Oxide Nanoparticles: An Update on Antimicrobial and Anticancer Activity

AbstractNanotechnology has changed and developed all the sectors and working fields. Nanoparticles are one of the important evolutionary materials that have application in almost all the working areas such as catalysis, bioengineering, photoelectricity, antibacterial, anticancer, and medical imaging due to their unique physical and chemical properties. Traditionally used chemical and physical method of synthesis of nanoparticles have several disadvantages like using different chemicals, high cost, and most importantly they are hazardous to the environment. Counter to these disadvantages, a more eco‐friendly, easy, and cost‐effective green synthesis method is widely employed nowadays. Various parts of a plant are used as a fuel for reducing the metal ion salt. Plant extracts act as reducing, stabilizing, and capping agents. Besides these advantages, photosynthesized nanoparticles are nontoxic, more stable, and more uniform in size than their counterparts prepared by the traditional method. In this present review, the synthesis of various plant extract‐mediated metal and metal oxide nanoparticles is discussed along with their different applications. This review provides a comprehensive overview of key findings in green synthesis of metal and metal oxide nanoparticles and attempts to determine their possible synthesis mechanism. This article also focuses on factors affecting their synthesis, characterization, potential applications, and prospects.

Computed tomography employing sensing of high energy particle current

Objective. We demonstrate High Energy Current Computed Tomography (HEC-CT) employing megavoltage linac x-rays.Approach. Using deterministic radiation transport we simulate two-parameter HEC-CT projections and using inverse Fourier transform we reconstruct two distinct material parameters for water phantoms with ICRP tissue inserts and materials of different atomic number Z. The HEC-CT projections are obtained by beam scanning and rotating the object.Main Results. The first HEC-CT material parameter is alike the standard attenuation coefficient with dependence on atomic number and material density similar to the Hounsfield Units. The second material parameter has opposite trends and does not find any analogy in the standard CT framework.Significance. New CT method has been invented for medical imaging or non-destructive testing. The key feature of the technique is a two-value CT reconstruction based on particle current instead of transmitted dose.

Automatic semantic segmentation of breast cancer in DCE-MRI using DeepLabV3+ with modified ResNet50

Research on breast cancer segmentation is essential due to its high prevalence as the most common cancer in women and its occurrence in men as well. Breast cancer involves abnormal cell growth in the breast, highlighting the importance of advanced imaging. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is an effective technique for this purpose. Deep learning has significantly influenced medical imaging in recent years, especially in accurately segmenting tumors from MRI images. Two techniques have been proposed for breast tumor segmentation: Dilated ResNet50 (RN50D) and Parallel Layers Added ResNet50 (PLA-RN50). RN50D involves altering the dilation factor of the convolution layer within the residual block of ResNet50. PLA-RN50 entails the integration of parallel layers following the final residual block of the ResNet50 architecture. The modified architectures serve as the backbone for the DeepLabV3+ network. The DeepLabV3+ with RN50D or PLA-RN50D is a powerful and effective architecture that integrates deep feature extraction, multiscale spatial information, and precise segmentation to achieve high accuracy in lesion segmentation for breast DCE-MRI images. The proposed technique is tested on a QIN Breast DCE-MRI dataset comprising 233 images sourced from The Cancer Image Archive. The proposed method achieves a dice score of 0.92. The superior segmentation performance of DeepLabV3+ with PLA-RN50, as compared to its counterparts using ResNet18 and ResNet50, highlights the impactful modifications incorporated in PLA-RN50 for optimizing breast tumor segmentation.

Objective quality assessment of medical images and videos: review and challenges

Objective quality assessment of medical images and videos: review and challenges

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The reconstruction of MR images has always been a challenging inverse problem in medical imaging. Acceleration of MR scanning is of great importance for clinical research and cutting-edge applications. One of the primary efforts to achieve this is using compressed sensing (CS) theory. The CS aims to reconstruct MR images using a small number of sampled data in k-space. The CS-MRI techniques face challenges, including the potential loss of fine structure and increased computational complexity. We introduce a novel framework based on a regularized sparse recovery problem and a sharpening step to improve the CS-MRI approaches regarding fine structure loss under high acceleration factors. This problem is solved via the Half Quadratic Splitting (HQS) approach. The inverse problem for reconstructing MR images is converted into two distinct sub-problems, each of which can be solved separately. One key feature of the proposed approach is the replacement of one sub-problem with a denoiser. This regularization assists the optimization of the Smoothed \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\ell}_{0}$$\\end{document} (SL0) norm in escaping local minimums and enhances its precision. The proposed method consists of smoothing, feature modification, and Smoothed \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\ell}_{0}$$\\end{document} cost function optimization. The proposed approach improves the SL0 algorithm for MRI reconstruction without complicating it. The convergence of the proposed approach is illustrated analytically. The experimental results show an acceptable performance of the proposed method compared to the network-based approaches.

Prognostic evaluation using radiomics after stereotactic body radiotherapy in early-stage lung cancer

Non-small cell lung cancer (NSCLC), the leading cause of cancer-related deaths, is the most common subtype of lung cancer with an incidence of 85%. Stereotactic body radiotherapy (SBRT) is a curative treatment option for patients with early-stage NSCLC who cannot undergo surgery due to medical reasons or who refuse surgery. Radiomics non-invasively extracts advanced imaging features invisible to the human eye from medical images. Radiomics has prognostic value in predicting oncological outcomes after lung SBRT. Although studies on this subject are available in the literature, they are quite heterogeneous. There is a need for large-scale multicenter studies in which standard imaging techniques are used to obtain radiomic features, artificial intelligence-based segmentations are used to eliminate differences between contours, and SBRT dose schemes with appropriate therapeutic indexes are applied. This review aimed to interpret the existing studies and emphasize the clinical importance of radiomics, which can contribute to personalized treatment. A comprehensive literature search was conducted through the PubMed database using a wide range of keywords, which yielded 11 peer-reviewed articles published between 2017 and 2024. Seven articles evaluated computed tomography radiomics, and four evaluated fluorodeoxyglucose positron emission tomography-computed tomography radiomics. Oncological outcomes are not always identical in patients with a similar history receiving similar treatments at the same stage and age. Clinical, demographic, or treatment-related data are insufficient to predict prognosis and determine personalized treatment. Incorporating radiomics to these data can help establish models with higher accuracy and achieve personalized treatment.

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.

Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques, including thresholding, edge-based methods, region-based approaches, clustering, and graph-based segmentation. While these methods are computationally efficient and interpretable, they often face significant challenges when applied to complex, noisy, or variable medical images. The central focus of this review is the transformative impact of deep learning on medical image segmentation. We delve into prominent deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, Recurrent Neural Networks (RNNs), Adversarial Networks (GANs), and Autoencoders (AEs). Each architecture is analyzed in terms of its structural foundation and specific application to medical image segmentation, illustrating how these models have enhanced segmentation accuracy across various clinical contexts. Finally, the review examines the integration of deep learning with traditional segmentation methods, addressing the limitations of both approaches. These hybrid strategies offer improved segmentation performance, particularly in challenging scenarios involving weak edges, noise, or inconsistent intensities. By synthesizing recent advancements, this review provides a detailed resource for researchers and practitioners, offering valuable insights into the current landscape and future directions of medical image segmentation.

MFHARFNet: Multi-branch feature hybrid and adaptive receptive field network for image segmentation

Abstract Accurate segmentation of medical images is crucial for disease diagnosis and understanding disease changes. Deep learning methods, utilizing encoder-decoder structures, have demonstrated cutting-edge performance in various medical image segmentation tasks. However, the pooling operation in the encoding stage results in feature loss, which makes the network lack the ability to fuse multi-scale information at different levels, hinders its effective perception of multi-scale information, and leads to poor segmentation performance. Drawing inspiration from the U-shaped network, this study introduces a multi-branch feature hybrid attention and adaptive receptive field network (MFHARFNet) for medical image segmentation. Building upon the encoder-decoder framework, we initially devise a multi-branch feature hybrid attention module (MFHAM) to seamlessly integrate feature maps of varying scales, capturing both fine-grained features and coarse-grained semantics across the entire scale. Furthermore, we redesign the skip connection to amalgamate feature information from different branches in the encoder stage and efficiently transmit it to the decoder, providing the decoder with global context feature maps at different levels. Finally, the adaptive receptive field (ARF) module is introduced in the decoder feature reconstruction stage to adapt and focus on related fields, ensuring the model's adaptation to different segmentation target features, and achieving different weights for the output of different convolution kernels to improve segmentation performance. We comprehensively evaluate our method on medical image segmentation tasks, by using four public datasets across CT and MRI. Remarkably, MFHARFNet method consistently outperforms other state-of-the-art methods, exceeding UNet by 2.1%, 0.9%, 6.6% and 1.0% on Dice on ATLAS, LiTs, BraTs2019 and Spine and intervertebral disc datasets, respectively. In addition, MFHARFNet minimizes network parameters and computational complexity as much as possible. The source codes are in https://github.com/OneHundred99/MFHARFNet.

FMRI-based Alzheimer’s disease detection via functional connectivity analysis: a systematic review

Alzheimer’s disease is a common brain disorder affecting many people worldwide. It is the primary cause of dementia and memory loss. The early diagnosis of Alzheimer’s disease is essential to provide timely care to AD patients and prevent the development of symptoms of this disease. Various non-invasive techniques can be utilized to diagnose Alzheimer’s in its early stages. These techniques include functional magnetic resonance imaging, electroencephalography, positron emission tomography, and diffusion tensor imaging. They are mainly used to explore functional and structural connectivity of human brains. Functional connectivity is essential for understanding the co-activation of certain brain regions co-activation. This systematic review scrutinizes various works of Alzheimer’s disease detection by analyzing the learning from functional connectivity of fMRI datasets that were published between 2018 and 2024. This work investigates the whole learning pipeline including data analysis, standard preprocessing phases of fMRI, feature computation, extraction and selection, and the various machine learning and deep learning algorithms that are used to predict the occurrence of Alzheimer’s disease. Ultimately, the paper analyzed results on AD and highlighted future research directions in medical imaging. There is a need for an efficient and accurate way to detect AD to overcome the problems faced by patients in the early stages.

SWOT analysis of the application of artificial intelligence in radiologic technology and radiology

Artificial intelligence (AI) is bringing changes to radiology and radiologic technology, enabling the development of programs and algorithms that facilitate diagnosis and decisions. It is essential to understand how AI can improve patient outcomes, increase the efficiency of investigations and reduce costs. Machine and deep learning have proven to be extremely useful in the detection and characterization of lesions, improving routine imaging techniques and facilitating the work of radiologists by reducing workload and improving the quality of reporting. The practical application of artificial intelligence in radiology and radiologic technology has been slowed down by the lack of integrated solutions and well-structured data archives, as well as challenges such as non-transparency of decision-making systems and large amounts of quality data needed to train artificial intelligence models. There are concerns about the potential impact of AI on the work of radiologists and radiologic technologists, which may hinder the development and implementation of this technology. Textual data derived from image reports can provide valuable healthcare insights. Natural language processing (NLP), a subset of artificial intelligence, offers promising solutions for handling unstructured text in these reports, opening a new era in extracting information from medical images and related reports. Due to the challenges in training experts for the application of AI in healthcare, a multidisciplinary approach will have to be used and investments in collaboration and education will have to be made. There are outstanding issues of liability and regulation regarding data storage and privacy, particularly in the case of cloud storage. The concern of the workforce and their lack of education about artificial intelligence represents an obstacle to its adoption, but also offers an opportunity for the technological advancement of the profession.

Addressing the Generalizability of AI in Radiology Using a Novel Data Augmentation Framework with Synthetic Patient Image Data: Proof-of-Concept and External Validation Classification Tasks in Multiple Sclerosis.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Artificial intelligence (AI) models often face performance drops after deployment to external datasets. This study evaluated the potential of a novel data augmentation framework based on generative adversarial networks (GAN) that creates synthetic patient image data during model training to improve model generalizability. Model development and external testing were performed for a given classification task, namely the detection of new fluid-attenuated inversion recovery (FLAIR) lesions on MRI during longitudinal follow-up of patients with multiple sclerosis (MS). An internal dataset of 669 patients with MS (n = 3083 examinations) was used to develop an attention-based network, trained both with and without the inclusion of the GAN-based synthetic data augmentation framework. External testing was performed on 134 patients with MS from a different institution, with MR images acquired using different scanners and protocols than images used during training. Models trained using synthetic data augmentation showed a significant performance improvement when applied on external data (AUC 83.6% without synthetic data versus AUC 93.3% with synthetic data augmentation, P = .03), achieving comparable results to the internal test set (AUC 95.5%, P = .53), whereas models without synthetic data augmentation demonstrated a performance drop upon external testing (AUC 93.8% on internal dataset versus AUC 83.6% on external data, P = .03). Data augmentation with synthetic patient data substantially improved performance of AI models on unseen MRI data and may be extended to other clinical conditions or tasks to mitigate domain shift, limit class imbalance, and enhance the robustness of AI applications in medical imaging. ©RSNA, 2024.

ShiftTransUNet: An Efficient Deep Learning Model for Medical Image Segmentation Using ShiftViT Framework

Deep learning has significantly advanced the field of medical image segmentation. However, the complexity of network structures often leads to high computational demands, limiting their practical efficiency. To enhance the efficiency of image segmentation, this paper introduces an innovative, concise, and lightweight deep learning network. First, to reduce model complexity, we replaced the attention mechanism in the traditional vision transformer (ViT) structure with a shift operation, creating the ShiftViT architecture. This substitution significantly decreased computation and the number of parameters while preserving model performance. Second, to retain and enhance fine-grained features and facilitate more precise information transfer across different layers, we employed a full-scale progressive skip connection strategy. This approach effectively integrates multi-scale feature information, further enhancing model performance. Additionally, to further reduce network complexity, inspired by the independence of probabilities, we opted for depth-wise separable convolution over traditional convolution. This enhances the relative independence between layers. Together, these modifications achieved superior segmentation results on both the Synapse and Automated Cardiac Diagnostic Challenge (ACDC) datasets compared to mainstream models, with substantial advantages in terms of computational efficiency and parameter count. The proposed approach represents an effective solution for medical image applications with limited computational resources and holds great promise for clinical practice.