The semantic segmentation of a brain tumor is the essential stage in medical treatment planning. Due to the different characteristics of tumors, one of the main difficulties in image segmentation is the severe imbalan...The semantic segmentation of a brain tumor is the essential stage in medical treatment planning. Due to the different characteristics of tumors, one of the main difficulties in image segmentation is the severe imbalance between classes. Also, a dataset with imbalanced classes is a common problem in multimodal 3D brain MRIs. Despite these problems, most studies in brain tumor segmentation are biased toward the overrepresented tumor class (majority class) and ignore the small size tumor class (minority class). In this paper, we propose an improved loss function Weighted Focal Loss (WFL), based on 3D U-Net to enhance the prediction of brain tumor segmentation. Using our proposed loss function (WFL) solves the imbalance between classes and the imbalance between weights by giving higher weights to the minority and lower weights to the majority. After assigning these weights to different pixel values, our work is able to resolve pixel degradation, which is one of the limitations of the loss function during model training. Based on our experiments, the proposed function (WFL) on the 3D U-Net model for high-grade glioma (HGG) and low-grade glioma (LGG) in the Brain Tumor Segmentation Challenge (BraTS) 2019 dataset has shown promising results for tumor core (TC), whole tumor (WT) and enhanced tumor (ET) with average dice scores of HGG: 0.830, 0.913, 0.815 and Dice scores of LGG for TC: 0.731, WT: 0.775 and ET: 0.685. Moreover, we deployed our training on BraTS 2020 in which we obtained mean Dice scores of HGG: TC: 0.843, WT: 0.892, ET: 0.871 and Dice scores of LGG: 0.7501, 0.7985, 0.6103 for TC, WT and ET, respectively.展开更多
Brain tumors are considered as most fatal cancers.To reduce the risk of death,early identification of the disease is required.One of the best available methods to evaluate brain tumors is Magnetic resonance Images(MRI...Brain tumors are considered as most fatal cancers.To reduce the risk of death,early identification of the disease is required.One of the best available methods to evaluate brain tumors is Magnetic resonance Images(MRI).Brain tumor detection and segmentation are tough as brain tumors may vary in size,shape,and location.That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’.So an automated brain tumor detection and segmentation is required.This work suggests a Region-based Convolution Neural Network(RCNN)approach for automated brain tumor identification and segmentation using MR images,which helps solve the difficulties of brain tumor identification efficiently and accurately.Our methodology is based on the accurate and efficient selection of tumorous areas.That reduces computational complexity and time.We have validated the designed experimental setup on a standard dataset,BraTS 2020.We used binary evaluation matrices based on Dice Similarity Coefficient(DSC)and Mean Average Precision(mAP).The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed method.The suggested approach attained an averageDSC of 0.92 andmAP 0.92 for 10 patients,while on the whole dataset,the scores are DSC 0.89 and mAP 0.90.The following results clearly show the performance efficiency of the proposed methodology.展开更多
<div style="text-align:justify;"> <strong><span style="font-family:Verdana;">Background:</span></strong><span style="font-family:Verdana;"> Intensity M...<div style="text-align:justify;"> <strong><span style="font-family:Verdana;">Background:</span></strong><span style="font-family:Verdana;"> Intensity Modulated Radiation Therapy (IMRT) is currently employed as a major arm of treatment in multiforme glioblastoma (GBM). The present study aimed to compare 3D-CRT with IMRT to assess tumor volume coverage and OAR sparing for </span><span style="font-family:Verdana;">the </span><span "=""><span style="font-family:Verdana;">treatment of malignant gliomas. </span><b><span style="font-family:Verdana;">Materials</span></b> <b><span style="font-family:Verdana;">and</span></b> <b><span style="font-family:Verdana;">methods:</span></b><span style="font-family:Verdana;"> We assessed 22 anonymized patients datasets with High Grade Glioblastoma who had undergone post</span></span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">operative Intensity Modulated Radiotherapy (IMRT) and 3D Conformal Radiotherapy (3D-CRT), This study will compare and contrast treatment plans Rapidarc and 3D-CRT to determine w</span><span style="font-family:Verdana;">h</span><span style="font-family:Verdana;">ich techn</span><span style="font-family:Verdana;">ology</span><span "=""><span style="font-family:Verdana;"> improves significantly dosimetric parameters. </span><b><span style="font-family:Verdana;">Results:</span></b><span style="font-family:Verdana;"> Plans will be assessed by reviewing the coverage of the PTV using mean, maximum and minimum doses while the OAR doses will be compared using the maximal doses for each, as set out in the QUANTEC dose limits. </span><b><span style="font-family:Verdana;">Conclusion:</span></b><span style="font-family:Verdana;"> The use of IMRT seems a superior technique as compared to 3D-CRT for the treatment of malignant gliomas having the potential to increase </span></span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">dose to the PTV while sparing OARs optimally.</span> </div>展开更多
文摘The semantic segmentation of a brain tumor is the essential stage in medical treatment planning. Due to the different characteristics of tumors, one of the main difficulties in image segmentation is the severe imbalance between classes. Also, a dataset with imbalanced classes is a common problem in multimodal 3D brain MRIs. Despite these problems, most studies in brain tumor segmentation are biased toward the overrepresented tumor class (majority class) and ignore the small size tumor class (minority class). In this paper, we propose an improved loss function Weighted Focal Loss (WFL), based on 3D U-Net to enhance the prediction of brain tumor segmentation. Using our proposed loss function (WFL) solves the imbalance between classes and the imbalance between weights by giving higher weights to the minority and lower weights to the majority. After assigning these weights to different pixel values, our work is able to resolve pixel degradation, which is one of the limitations of the loss function during model training. Based on our experiments, the proposed function (WFL) on the 3D U-Net model for high-grade glioma (HGG) and low-grade glioma (LGG) in the Brain Tumor Segmentation Challenge (BraTS) 2019 dataset has shown promising results for tumor core (TC), whole tumor (WT) and enhanced tumor (ET) with average dice scores of HGG: 0.830, 0.913, 0.815 and Dice scores of LGG for TC: 0.731, WT: 0.775 and ET: 0.685. Moreover, we deployed our training on BraTS 2020 in which we obtained mean Dice scores of HGG: TC: 0.843, WT: 0.892, ET: 0.871 and Dice scores of LGG: 0.7501, 0.7985, 0.6103 for TC, WT and ET, respectively.
基金This work was funded by the Ministry of Education under Grant NRF-2019R1A2C1006159Grant NRF-2021R1A6A1A03039493。
文摘Brain tumors are considered as most fatal cancers.To reduce the risk of death,early identification of the disease is required.One of the best available methods to evaluate brain tumors is Magnetic resonance Images(MRI).Brain tumor detection and segmentation are tough as brain tumors may vary in size,shape,and location.That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’.So an automated brain tumor detection and segmentation is required.This work suggests a Region-based Convolution Neural Network(RCNN)approach for automated brain tumor identification and segmentation using MR images,which helps solve the difficulties of brain tumor identification efficiently and accurately.Our methodology is based on the accurate and efficient selection of tumorous areas.That reduces computational complexity and time.We have validated the designed experimental setup on a standard dataset,BraTS 2020.We used binary evaluation matrices based on Dice Similarity Coefficient(DSC)and Mean Average Precision(mAP).The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed method.The suggested approach attained an averageDSC of 0.92 andmAP 0.92 for 10 patients,while on the whole dataset,the scores are DSC 0.89 and mAP 0.90.The following results clearly show the performance efficiency of the proposed methodology.
文摘<div style="text-align:justify;"> <strong><span style="font-family:Verdana;">Background:</span></strong><span style="font-family:Verdana;"> Intensity Modulated Radiation Therapy (IMRT) is currently employed as a major arm of treatment in multiforme glioblastoma (GBM). The present study aimed to compare 3D-CRT with IMRT to assess tumor volume coverage and OAR sparing for </span><span style="font-family:Verdana;">the </span><span "=""><span style="font-family:Verdana;">treatment of malignant gliomas. </span><b><span style="font-family:Verdana;">Materials</span></b> <b><span style="font-family:Verdana;">and</span></b> <b><span style="font-family:Verdana;">methods:</span></b><span style="font-family:Verdana;"> We assessed 22 anonymized patients datasets with High Grade Glioblastoma who had undergone post</span></span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">operative Intensity Modulated Radiotherapy (IMRT) and 3D Conformal Radiotherapy (3D-CRT), This study will compare and contrast treatment plans Rapidarc and 3D-CRT to determine w</span><span style="font-family:Verdana;">h</span><span style="font-family:Verdana;">ich techn</span><span style="font-family:Verdana;">ology</span><span "=""><span style="font-family:Verdana;"> improves significantly dosimetric parameters. </span><b><span style="font-family:Verdana;">Results:</span></b><span style="font-family:Verdana;"> Plans will be assessed by reviewing the coverage of the PTV using mean, maximum and minimum doses while the OAR doses will be compared using the maximal doses for each, as set out in the QUANTEC dose limits. </span><b><span style="font-family:Verdana;">Conclusion:</span></b><span style="font-family:Verdana;"> The use of IMRT seems a superior technique as compared to 3D-CRT for the treatment of malignant gliomas having the potential to increase </span></span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">dose to the PTV while sparing OARs optimally.</span> </div>
