Current Medical Imaging Reviews最新文献

{"title":"Sparse-View CT Joint Reconstruction Strategy with Sparse Sampling Encoding Layer.","authors":"Hu Guo, Minghan Yang, Ziheng Zhang, Haibo Yu, Shuai Chen, Jianye Wang, Minghao Li","doi":"10.2174/0115734056354972250221012822","DOIUrl":"https://doi.org/10.2174/0115734056354972250221012822","url":null,"abstract":"<p><strong>Background: </strong>Sparse angular projection is an important way to reduce CT dose. It consists of two processes, sparse sampling, and image reconstruction based on sparse projection. Under the traditional reconstruction framework, the sparseness of the projection angle may cause a degradation effect in the reconstructed image. A series of machine learning methods for sparse angle CT reconstruction developed in recent years, especially deep learning methods, can effectively improve the reconstruction quality, however, these methods can only reconstruct CT images based on a certain sparse sampling scheme.</p><p><strong>Objective: </strong>On the other words, they cannot search for an efficient sparse sampling scheme under a certain dose constraint automatically, which became the motivation to develop an end-to-end sparse angular CT reconstruction method.</p><p><strong>Methods: </strong>In this work, we propose a sampling encoding layer for searching sparse sampling schemes and integrate it into a sparse reconstruction neural network model based on projection data. Meanwhile, a joint reconstruction strategy based on both the radon domain and image domain painting is also developed.</p><p><strong>Results: </strong>Experiments based on public CT datasets demonstrate the effectiveness of the method.</p><p><strong>Conclusion: </strong>The results show that the joint reconstruction network based on a sparse sampling coding layer has great application potential.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recurrence of Pleomorphic Adenoma in the Submandibular Gland: A Case Report and Literature Review.","authors":"Zhiqiong Li, Guiying Yuan, Ye Zhang, Junbin Huang, Fan Xu, Yuchao Xiong, Xuwen Zeng","doi":"10.2174/0115734056333595250226071534","DOIUrl":"https://doi.org/10.2174/0115734056333595250226071534","url":null,"abstract":"<p><strong>Introduction: </strong>Recurrent pleomorphic adenoma (PA) in the submandibular gland is a rare tumor that may be misdiagnosed as an inflammatory lesion. The imaging manifestations of the submandibular gland recurrent PA are unclear, with only three case reports reporting CT and MRI imaging, respectively. Our report is the first case report that comprehensively describes the imaging manifestations of recurrent PA in the submandibular gland.</p><p><strong>Case presentation: </strong>A 28-year-old woman had a right submandibular gland pleomorphic adenoma that recurred 5 years after resection and gradually grew larger. She had no special discomfort and was diagnosed with a recurrence of pleomorphic adenoma. The patient underwent CT and MRI examinations and tumor resection, and postoperative pathology showed tumor recurrence.</p><p><strong>Conclusion: </strong>This case report provides substantial and comprehensive CT and MRI data, which is conducive to the diagnosis of the recurrence of submandibular gland pleomorphic adenoma and the avoidance of misdiagnosis to the greatest extent possible.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Severe Disseminated Cryptococcosis Leading to Multi-Organ Failure in a Renal Transplant Patient: A Case Report.","authors":"Daniela Sanchez-Lobo, Paulina Espinosa-Zerecero, Ricardo Cebrian-Garcia, Marcos Garcia-Nava, Fernando Cano-Garcia, Maria-Del-Carmen Garcia-Blanco, Ernesto Roldan-Valadez","doi":"10.2174/0115734056353324250302180953","DOIUrl":"https://doi.org/10.2174/0115734056353324250302180953","url":null,"abstract":"<p><p>Background Cryptococcosis is a severe but rare opportunistic fungal infection predominantly affecting immunocompromised individuals, such as posttransplant patients. The diagnosis is frequently delayed due to non-specific symptoms and lower incidence than other fungal infections. Case Report A case of a 50-year-old male renal transplant recipient who developed disseminated cryptococcosis complicated by multi-organ failure is presented. Despite adherence to international treatment guidelines, the patient's condition rapidly deteriorated due to the extensive immunosuppression required for transplant rejection management. The patient developed pneumonia and was diagnosed with disseminated cryptococcosis on the 10th day of hospitalization, with Cryptococcus gattii identified in the pulmonary system and pleura. The patient underwent multiple interventions, including bronchoscopy, lobectomy, and pneumonectomy. Despite aggressive treatment, the infection progressed, leading to severe complications, such as neurological decline, gastrointestinal bleeding, and ultimately, multi-organ failure. The patient passed away after 53 days of hospitalization. Conclusion This report highlights the importance of early diagnosis and multidisciplinary management in post-transplant patients with suspected opportunistic infections. The high mortality associated with disseminated cryptococcosis, particularly in severely immunosuppressed patients, underscores the need for vigilance and prompt intervention to improve patient outcomes.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Primary Cardiac Angiosarcoma Diagnosed by Multimodality Imaging: A Case Report : Multimodality Imaging of Cardiac Angiosarcoma.","authors":"Qin Zhang, Shuying Luo, Hua Ye, Tao Yang, Tijiang Zhang, Bangguo Li, Hong Yu","doi":"10.2174/0115734056352556250226080204","DOIUrl":"https://doi.org/10.2174/0115734056352556250226080204","url":null,"abstract":"<p><strong>Background: </strong>Primary cardiac tumors are rare. Most primary cardiac tumors are benign, with approximately 10.83% being malignant. We present a rare case of Primary Cardiac Angiosarcoma (PCA) with multiple metastases diagnosed using multimodality imaging, to enhance the understanding of PCA among clinicians and radiologists.</p><p><strong>Case description: </strong>A 29-year-old woman presented to our hospital with a 2-day history of chest tightness, chest pain, palpitations, and dyspnea after physical activity. Ultrasonography and Computed Tomography (CT) of the heart revealed a mass in the right atrium. Cardiac magnetic resonance imaging suggested either a large cardiac lymphoma or angiosarcoma. The histopathological diagnosis confirmed a cardiac angiosarcoma. Positron Emission Tomography-Computed Tomography (PET/CT) revealed intense 18F-fluorodeoxyglucose (18F-FDG) uptake in the right side of the heart, with a maximum standardized uptake value of 10.9. Three months later, the patient was re-examined using abdominal CT, echocardiography, and PET/CT. PET/CT revealed increased 18F-FDG uptake which had become more extensive, with multifocal metastatic nodules in both the lungs and mediastinum. The patient was lost to follow-up after being discharged on May 1, 2022.</p><p><strong>Conclusion: </strong>The combined evaluation using multimodality imaging plays a vital role in determining the precise size and localization of the PCA, detecting distant metastases, and assessing patient prognosis.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation between Liver fat Content Determined by Ultrasonic Attenuation Imaging and Lipid Metabolism in Patients with Non-Alcoholic Fatty Liver Disease.","authors":"Yanhong Hao, Yanjing Zhang, Guolin Yin, Lei Zhang, Liping Liu","doi":"10.2174/0115734056335310250217064323","DOIUrl":"https://doi.org/10.2174/0115734056335310250217064323","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to investigate the utility of ultrasonic attenuation imaging (ATI) in assessing the relationship between hepatic fat content and lipid metabolism in patients diagnosed with type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD).</p><p><strong>Methods: </strong>239 patients diagnosed with T2DM were included, with liver fat quantified using proton density fat fraction (PDFF). We analyzed the variance in ATI across various grades of fatty liver and its correlation with clinical parameters. Additionally, a receiver operating characteristic curve (ROC) was employed to evaluate the diagnostic accuracy of ATI for different degrees of fatty liver, determining optimal diagnostic thresholds while calculating sensitivity and specificity. Furthermore, we assessed the reliability of ATI and SWE in measuring liver acoustic attenuation and elastic stiffness using the intraclass correlation coefficient (ICC).</p><p><strong>Results: </strong>We observed significant variations in ATI across different grades of fatty liver (p<0.001). ATI exhibited positive correlations with SWE, BMI, GLU (OH), steatosis grade, ALT, TG, and UA, while demonstrating a negative correlation with HDL-c. Notably, the correlation coefficient with steatosis grade was 0.76, indicating a strong association. The equation for the stepwise multiple linear regression model used is as follows: ATI=0.338+0.014×TG+0.052×BMI+0.001×ALT+0.113×SWE. AUROCs indicated the best cutoffs for ATI in different degrees of steatosis to be as follows: ≥ S1 = 0.665 dB·cm-1·MHz-1 (AUC = 0.857); ≥ S2 = 0.705 dB·cm-1·MHz-1 (AUC = 0.921); ≥ S3 = 0.745 dB·cm-1·MHz-1 (AUC = 0.935). The ICC values for ATI and SWE in liver-mimicking measurements exceeded 0.75 (p<0.001), signifying excellent repeatability.</p><p><strong>Conclusion: </strong>The ATI could quantitatively assess the severity of fatty liver, enabling effective identification of patients suitable for liver biopsy referral.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"YOLOv8 Algorithm-aided Detection of Rib Fracture on Multiplane Reconstruction Images.","authors":"Shihong Liu, Wei Zhang, Gang Wu","doi":"10.2174/0115734056337623250212052347","DOIUrl":"https://doi.org/10.2174/0115734056337623250212052347","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to develop and assess the performance of a YOLOv8 algorithm-aided detection model for identifying rib fractures on multiplane reconstruction (MPR) images, addressing the limitations of current AI models and the labor-intensive nature of manual diagnosis.</p><p><strong>Methods: </strong>Ethical approval was obtained, and a dataset comprising 624 MPR images, confirmed by CT, was collected from three regions of Tongji Hospital between May 2020 and May 2023. The images were categorized into training, validation, and external test sets. A musculoskeletal radiologist labeled the images, and a YOLOV8n model was trained and validated using these datasets. The performance metrics, including sensitivity, specificity, accuracy, precision, recall, and F1 score, were calculated.</p><p><strong>Results: </strong>The refined YOLO model demonstrated high diagnostic accuracy, with sensitivity, specificity, and accuracy rates of 96%, 97%, and 97%, respectively. The AI model significantly outperformed the radiologist in terms of diagnostic speed, with an average interpretation time of 2.02 seconds for 144 images compared to 288 seconds required by the radiologist.</p><p><strong>Conclusion: </strong>The YOLOv8 algorithm shows promise in expediting the diagnosis of rib fractures on MPR images with high accuracy, potentially improving clinical efficiency and reducing the workload for radiologists. Future work will focus on enhancing the model with more feature learning capabilities and integrating it into the PACS system for human-computer interaction.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Machine Learning Model Based on Multi-Phase Contrast-enhanced CT for the Preoperative Prediction of the Muscle-Invasive Status of Bladder Cancer.","authors":"Xucheng He, Yuqing Chen, Shanshan Zhou, Guisheng Wang, Rongrong Hua, Caihong Li, Yang Wang, Xiaoxia Chen, Ju Ye","doi":"10.2174/0115734056377754250304040058","DOIUrl":"https://doi.org/10.2174/0115734056377754250304040058","url":null,"abstract":"<p><strong>Background: </strong>Muscle infiltration of bladder cancer has become the most important index to evaluate its prognosis. Machine learning is expected to accurately identify its muscle infiltration status on images.</p><p><strong>Objective: </strong>This study aimed to establish and validate a machine learning prediction model based on multi-phase contrast-enhanced CT (MCECT) for preoperatively evaluating the muscle-invasive status of bladder cancer.</p><p><strong>Methods: </strong>A retrospective study was conducted on bladder cancer patients who underwent surgical resection and pathological confirmation by MCECT scans. They were randomly divided into training and testing groups at a ratio of 8:2; we used an independent external testing set for verification. The radiomics features of lesions were extracted from MCECT images and radiomics signatures were established by dual sample T-test and least absolute shrinkage selection operator (LASSO) regression. Afterward, four machine learning classifier models were established. The receiver operating characteristic (ROC) curve, calibration, and decision curve analysis were employed to evaluate the efficiency of the model and analyze diagnostic performance using accuracy, precision, sensitivity, specificity, and F1-score.</p><p><strong>Results: </strong>The best predictive model was found to have logic regression as the classifier. The AUC value was 0.89 (5-fold cross-validation range 0.83-0.96) in the training group, 0.80 in the test group, and 0.87 in the external testing group. In the testing and external testing group, the diagnostic accuracy, precision, sensitivity, specificity, and F1-score were 0.759, 0.826, 0.863, 0.729, 0.785, and 0.794, 0.755, 0.953, 0.720, and 0.809, respectively.</p><p><strong>Conclusion: </strong>The machine learning model showed good accuracy in predicting the muscle infiltration status of bladder cancer before surgery.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fetal Diagnostics using Vision Transformer for Enhanced Health and Severity Prediction in Ultrasound Imaging.","authors":"Eshika Jain, Pratham Kaushik, Vinay Kukreja, Sakshi, Ayush Dogra, Bhawna Goyal","doi":"10.2174/0115734056360199250227053012","DOIUrl":"https://doi.org/10.2174/0115734056360199250227053012","url":null,"abstract":"<p><strong>Aim: </strong>This research aims to develop and evaluate a novel health classification and severity detection system based on Vision Transformers (ViTs) for fetal ultrasound imagery. This contributes to improved precision in fetal health status detection and abnormalities with more accurate results than other traditional models.</p><p><strong>Background: </strong>Amidst the other imperatives of resource-deficient developing nations, mitigating neonatal mortality rates is a challenge that demands precisionbased solutions in the era of artificial intelligence. Though the advent of machine learning models has added an optimal dimension to deal with emerging complexity in fetal ultrasound imagery, there is a call to address the huge gap in the demanded precision for prediction than the existing interpretation.</p><p><strong>Objective: </strong>This research strives to formulate and access a novel health classification and severity detection system based on the implementation of the Vision Transformers frameworks. This pioneering investigation represents an unparalleled exploration into the efficacy of ViTs for discerning intricate patterns within fetal ultrasonographic imagery, facilitating precise categorization of fetal well-being and prognosticating the magnitude of potential anomalies.</p><p><strong>Methodology: </strong>A private and confidential dataset of 500 fetal ultrasound images has been collected from diverse hospitals. Each image has been annotated by radiologists according to two main labels: the health status of the fetus, which includes healthy, mild, moderate, or severe, and the severity of abnormalities as a continuous measure. At different levels, the dataset underwent pre-processing via distinct techniques. Then, the composite loss function Cross-Entropy has been deployed to train the optimized VIT model using the Adam algorithm.</p><p><strong>Results: </strong>The classification accuracy of the proposed model is 90% for detecting the severity with an F1-score of 0.87 and MAE of 0.30. The research ascertained that the model ViT evinced a superlative efficacy for the capturing of fine-grained spatial relations in ultrasound images to produce revolutionary predictions.</p><p><strong>Conclusion: </strong>These results emphasize that ViTs have the potential to revolutionize fetal health monitoring and will contribute significantly to reducing neonatal mortality by supplying clinicians with accurate and reliable predictions for early interventions. This work stands as a yardstick for further diagnostic applications using AI in fetal health care.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer through Shear-Wave Elastography.","authors":"Qingfu Qian, Minling Zhuo, Yue Yu, Xiaodong Lin, Ensheng Xue, Zhikui Chen","doi":"10.2174/0115734056327323250108055841","DOIUrl":"https://doi.org/10.2174/0115734056327323250108055841","url":null,"abstract":"<p><strong>Background: </strong>There remains a lack of methods to accurately assess the efficacy of neoadjuvant chemoradiotherapy for locally advanced rectal cancer.</p><p><strong>Objective: </strong>This study aimed to investigate the value of shear-wave elastography in evaluating the treatment response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer.</p><p><strong>Materials and methods: </strong>This prospective observational study enrolled 275 patients with locally advanced rectal cancer who received neoadjuvant chemoradiotherapy during September 2021-March 2023. All patients underwent endorectal ultrasound and shear-wave elastography examination before total mesorectal excision. Clinical baseline data, endorectal ultrasound, and shear-wave elastography examination data were collected from all patients. The independent predictors of complete response were analyzed and screened, followed by the establishment of a logistic regression model. The diagnostic efficacy of the model was compared with that of radiologists.</p><p><strong>Results: </strong>The results of binary multivariate logistic regression suggested that the mean elastography value of the tumor lesion acted as an independent predictor for the diagnosis of complete response [OR: 0.894 (0.816, 0.981)]. The optimal cutoff value was 14.6 kPa. The area under the receiver operating characteristic curve of the model for predicting complete response in the training and test cohorts was 0.850 and 0.824, respectively. The diagnostic accuracy of the model was significantly higher than that of radiologists (P < 0.001).</p><p><strong>Conclusion: </strong>Shear-wave elastography can be used as a feasible method to evaluate the complete response of locally advanced rectal cancer after neoadjuvant chemoradiotherapy.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Segmented MR Images by RG-FCM subjected to Non-Uniform Compression comprising Cascade of different Encoders.","authors":"Lovepreet Singh Brar, Sunil Agrawal, Jaget Singh, Ayush Dogra","doi":"10.2174/0115734056356911250220124124","DOIUrl":"https://doi.org/10.2174/0115734056356911250220124124","url":null,"abstract":"<p><strong>Introduction: </strong>The fundamental problem with the transmission and storage of medical images is their inherent redundancy and large size necessitating higher bandwidth and a significant amount of storage space.</p><p><strong>Objectives: </strong>The main objective is to enhance the compression efficiency through accurate segmentation followed by non-uniform compression through a cascade of encoders.</p><p><strong>Background: </strong>Due to a sharp growth in digital imaging data, it is highly desirable to reduce the size of medical images by a significant amount, without losing clinically important diagnostic information. The majority of the compression techniques reported in the literature use either manual or traditional segmentation techniques to extract the informative parts of the images. The methods based upon non-uniform compression require accurate extraction of the informative part of the image to achieve higher compression rate.</p><p><strong>Methods: </strong>This research proposes unsupervised machine learning modified fuzzy c-means (FCM) clustering-based segmentation for accurate extraction of informative parts of MR images. The spatial constraints of the images are extracted using an automated region-growing algorithm and incorporated into the objective function of FCM clustering (RG-FCM) to enhance the performance of the segmentation process even in the presence of noise. Further, informative and background parts are subjected to two separate series of encoders, with higher bit rates for the informative part of the image.</p><p><strong>Results: </strong>Empirical analysis was done on the Magnetic Resonance Imaging (MRI)dataset, and experimental results indicate that the proposed technique outperforms similar existing techniques in terms of segmentation and compression metrics.</p><p><strong>Conclusion: </strong>This integration of different segmentation techniques exhibits improvement in Jaccard and dice indexes, and cascade of different encoders endorse the superior performance of the proposed compression technique. The proposed technique can help in achieving higher compression of medical images without compromising clinically significant information.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}