Medical image segmentation has consistently been a significant topic of research and a prominent goal,particularly in computer vision.Brain tumor research plays a major role in medical imaging applications by providin...Medical image segmentation has consistently been a significant topic of research and a prominent goal,particularly in computer vision.Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation.To prevent or minimize manual segmentation error,automated tumor segmentation,and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures.Many methods for detection and segmentation presently exist,but all lack high accuracy.This paper’s key contribution focuses on evaluating machine learning techniques that are supposed to reduce the effect of frequently found issues in brain tumor research.Furthermore,attention concentrated on the challenges related to level set segmentation.The study proposed in this paper uses the Population-based Artificial Bee Colony Clustering(P-ABCC)methodology to reliably collect initial contour points,which helps minimize the number of iterations and segmentation errors of the level-set process.The proposed model measures cluster centroids(ABC populations)and uses a level-set approach to resolve contour differences as brain tumors vary as they have irregular form,structure,and volume.The suggested model comprises of three major steps:first,pre-processing to separate the brain from the head and improves contrast stretching.Secondly,P-ABCC is used to obtain tumor edges that are utilized as an initial MRI sequence contour.The level-set segmentation is then used to detect tumor regions from all volume slices with fewer iterations.Results suggest improved model efficiency compared to state-of-the-art methods for both datasets BRATS 2019 and BRATS 2017.At BRATS 2019,dice progress was achieved for Entire Tumor(WT),Tumor Center(TC),and Improved Tumor(ET)by 0.03%,0.03%,and 0.01%respectively.At BRATS 2017,an increase in precision for WT was reached by 5.27%.展开更多
Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep ...Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep learning, particularly 3D U-Net architectures, has revolutionised medical image analysis by leveraging volumetric data to capture spatial context, enhancing segmentation accuracy. This paper reviews brain tumour segmentation methods, emphasising 3D U-Net advancements. We analyse contributions from the Brain Tumour Segmentation (BraTS) challenges (2014-2023), highlighting key improvements and persistent challenges, including tumour heterogeneity, limited annotated data, varied imaging protocols, computational constraints, and model generalisation. Unlike previous reviews, we synthesise these challenges, proposing targeted research directions: enhancing model robustness through domain adaptation and multi-institutional data sharing, developing lightweight architectures for clinical deployment, integrating multi-modal and clinical data, and incorporating explainability techniques to build clinician trust. By addressing these challenges, we aim to guide future research toward developing more robust, generalisable, and clinically applicable segmentation models, ultimately improving patient outcomes in neuro-oncology.展开更多
目的对比3D动脉自旋标记(ASL)与动态磁敏感对比成像(DSC)MRI灌注成像技术在颅内肿瘤的应用价值。方法 16例脑肿瘤病变患者,其中胶质瘤6例,转移瘤1例,脑膜瘤5例,梭形细胞肉瘤1例,小脑血管母细胞瘤2例,淋巴瘤1例。所有病例均经手术病理证...目的对比3D动脉自旋标记(ASL)与动态磁敏感对比成像(DSC)MRI灌注成像技术在颅内肿瘤的应用价值。方法 16例脑肿瘤病变患者,其中胶质瘤6例,转移瘤1例,脑膜瘤5例,梭形细胞肉瘤1例,小脑血管母细胞瘤2例,淋巴瘤1例。所有病例均经手术病理证实。用ASL技术和DSC 2种方法进行MR灌注成像,比较2种方法对脑肿瘤灌注表现的差异。结果 3D ASL所测脑血流量(CBF)与DSC所测相对脑血流量(rCBF)值两者有显著相关性,在评价肿瘤血供方面,3D ASL敏感性更优于DSC。结论 3D ASL技术在评价脑肿瘤疾病的脑组织局部血流灌注方面具有良好的应用价值及潜力。展开更多
文摘Medical image segmentation has consistently been a significant topic of research and a prominent goal,particularly in computer vision.Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation.To prevent or minimize manual segmentation error,automated tumor segmentation,and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures.Many methods for detection and segmentation presently exist,but all lack high accuracy.This paper’s key contribution focuses on evaluating machine learning techniques that are supposed to reduce the effect of frequently found issues in brain tumor research.Furthermore,attention concentrated on the challenges related to level set segmentation.The study proposed in this paper uses the Population-based Artificial Bee Colony Clustering(P-ABCC)methodology to reliably collect initial contour points,which helps minimize the number of iterations and segmentation errors of the level-set process.The proposed model measures cluster centroids(ABC populations)and uses a level-set approach to resolve contour differences as brain tumors vary as they have irregular form,structure,and volume.The suggested model comprises of three major steps:first,pre-processing to separate the brain from the head and improves contrast stretching.Secondly,P-ABCC is used to obtain tumor edges that are utilized as an initial MRI sequence contour.The level-set segmentation is then used to detect tumor regions from all volume slices with fewer iterations.Results suggest improved model efficiency compared to state-of-the-art methods for both datasets BRATS 2019 and BRATS 2017.At BRATS 2019,dice progress was achieved for Entire Tumor(WT),Tumor Center(TC),and Improved Tumor(ET)by 0.03%,0.03%,and 0.01%respectively.At BRATS 2017,an increase in precision for WT was reached by 5.27%.
文摘Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep learning, particularly 3D U-Net architectures, has revolutionised medical image analysis by leveraging volumetric data to capture spatial context, enhancing segmentation accuracy. This paper reviews brain tumour segmentation methods, emphasising 3D U-Net advancements. We analyse contributions from the Brain Tumour Segmentation (BraTS) challenges (2014-2023), highlighting key improvements and persistent challenges, including tumour heterogeneity, limited annotated data, varied imaging protocols, computational constraints, and model generalisation. Unlike previous reviews, we synthesise these challenges, proposing targeted research directions: enhancing model robustness through domain adaptation and multi-institutional data sharing, developing lightweight architectures for clinical deployment, integrating multi-modal and clinical data, and incorporating explainability techniques to build clinician trust. By addressing these challenges, we aim to guide future research toward developing more robust, generalisable, and clinically applicable segmentation models, ultimately improving patient outcomes in neuro-oncology.
文摘目的对比3D动脉自旋标记(ASL)与动态磁敏感对比成像(DSC)MRI灌注成像技术在颅内肿瘤的应用价值。方法 16例脑肿瘤病变患者,其中胶质瘤6例,转移瘤1例,脑膜瘤5例,梭形细胞肉瘤1例,小脑血管母细胞瘤2例,淋巴瘤1例。所有病例均经手术病理证实。用ASL技术和DSC 2种方法进行MR灌注成像,比较2种方法对脑肿瘤灌注表现的差异。结果 3D ASL所测脑血流量(CBF)与DSC所测相对脑血流量(rCBF)值两者有显著相关性,在评价肿瘤血供方面,3D ASL敏感性更优于DSC。结论 3D ASL技术在评价脑肿瘤疾病的脑组织局部血流灌注方面具有良好的应用价值及潜力。
