Pub Date : 2018-10-01Epub Date: 2018-12-17DOI: 10.1109/WNYIPW.2018.8576421
S M Kamrul Hasan, Cristian A Linte
The detection and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) is a very challenging task, despite the availability of modern medical image processing tools. Neuro-radiologists still diagnose deadly brain cancers such as even glioblastoma using manual segmentation. This approach is not only tedious, but also highly variable, featuring limited accuracy and precision, and hence raising the need for more robust, automated techniques. Deep learning methods such as the U-Net deep convolutional neural networks have been widely used in biomedical image segmentation. Although this model was demonstrated to yield desirable results on the BRATS 2015 dataset by using a pixel-wise segmentation map of the input image as an auto-encoder, which assures best segmentation accuracy, the output only showed limited accuracy and robustness for a number of cases. The goal of this work was to improve the U-net model by replacing the de-convolution component with an up- sampled by the Nearest-neighbor algorithm and also employing an elastic transformation to augment the training dataset to render the model more robust, especially for the segmentation of low-grade tumors. The proposed Nearest-Neighbor Re-sampling Based Elastic-Transformed (NNRET) U-net Deep CNN framework has been trained on 285 glioma patients BRATS 2017 MR dataset available through the MICCAI 2017 grand challenge. The framework has been tested on 146 patients using Dice similarity coefficient (DSC) & Intersection over Union (IoU) performance metrics and outweighed the classic U-net model.
{"title":"A Modified U-Net Convolutional Network Featuring a Nearest-neighbor Re-sampling-based Elastic-Transformation for Brain Tissue Characterization and Segmentation.","authors":"S M Kamrul Hasan, Cristian A Linte","doi":"10.1109/WNYIPW.2018.8576421","DOIUrl":"https://doi.org/10.1109/WNYIPW.2018.8576421","url":null,"abstract":"The detection and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) is a very challenging task, despite the availability of modern medical image processing tools. Neuro-radiologists still diagnose deadly brain cancers such as even glioblastoma using manual segmentation. This approach is not only tedious, but also highly variable, featuring limited accuracy and precision, and hence raising the need for more robust, automated techniques. Deep learning methods such as the U-Net deep convolutional neural networks have been widely used in biomedical image segmentation. Although this model was demonstrated to yield desirable results on the BRATS 2015 dataset by using a pixel-wise segmentation map of the input image as an auto-encoder, which assures best segmentation accuracy, the output only showed limited accuracy and robustness for a number of cases. The goal of this work was to improve the U-net model by replacing the de-convolution component with an up- sampled by the Nearest-neighbor algorithm and also employing an elastic transformation to augment the training dataset to render the model more robust, especially for the segmentation of low-grade tumors. The proposed Nearest-Neighbor Re-sampling Based Elastic-Transformed (NNRET) U-net Deep CNN framework has been trained on 285 glioma patients BRATS 2017 MR dataset available through the MICCAI 2017 grand challenge. The framework has been tested on 146 patients using Dice similarity coefficient (DSC) & Intersection over Union (IoU) performance metrics and outweighed the classic U-net model.","PeriodicalId":92712,"journal":{"name":"Proceedings. IEEE Western New York Image and Signal Processing Workshop","volume":"2018 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/WNYIPW.2018.8576421","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37350373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01Epub Date: 2018-12-17DOI: 10.1109/WNYIPW.2018.8576438
Dawei Liu, Isabelle Peck, Shusil Dangi, Karl Q Schwarz, Cristian A Linte
Left ventricular ejection fraction (LVEF) is a critical measure of cardiac health commonly acquired in clinical practice, which serves as the basis for cardiovascular therapeutic treatment. Ultrasound (US) imaging of the heart is the most common, least expensive, reliable and non-invasive modality to assess LVEF. Cardiologists, in practice, persistently use 2D US images to provide visual estimates of LVEF, which are based on 2D information embedded in the US images by examining the area changes in LV blood pool between diastole and systole. There has been some anecdotal evidence that visual estimation of the LVEF based on the area changes of the LV blood pool significantly underestimate true LVEF. True LVEF should be calculated based on changes in LV volumes between diastole and systole. In this project, we utilized both idealized models of the LV geometry - a truncated prolate spheroid (TPS) and a paraboloid model - to represent the LV anatomy. Cross-sectional areas and volumes of simulated LV shapes using both models were calculated to compare the LVEF. Further, a LV reconstruction algorithm was employed to build the LV blood pool volume in both systole and diastole from multi-plane 2D US imaging data. Our mathematical models yielded an area-based LVEF of 41 4.7% and a volume-based LVEF of 55 ±5.7%, while the 3D recon-struction model showed an area-based LVEF of 35 11.9% and a volume-based LVEF of 48.0 ± 14.0%. In summary, the area-based LVEF using all three models ±underestimate the volume-based LVEF using corresponding models by 13% to 14%. This preliminary study confirms both mathematically and empirically that area-based LVEF estimates indeed underestimate the true volume-based LVEF measurements and suggests that true volumetric measurements of the LV blood pool must be computed to correctly assess cardiac LVEF.
{"title":"LEFT VENTRICULAR EJECTION FRACTION: COMPARISON BETWEEN TRUE VOLUME-BASED MEASUREMENTS AND AREA-BASED ESTIMATES.","authors":"Dawei Liu, Isabelle Peck, Shusil Dangi, Karl Q Schwarz, Cristian A Linte","doi":"10.1109/WNYIPW.2018.8576438","DOIUrl":"https://doi.org/10.1109/WNYIPW.2018.8576438","url":null,"abstract":"<p><p>Left ventricular ejection fraction (LVEF) is a critical measure of cardiac health commonly acquired in clinical practice, which serves as the basis for cardiovascular therapeutic treatment. Ultrasound (US) imaging of the heart is the most common, least expensive, reliable and non-invasive modality to assess LVEF. Cardiologists, in practice, persistently use 2D US images to provide visual estimates of LVEF, which are based on 2D information embedded in the US images by examining the area changes in LV blood pool between diastole and systole. There has been some anecdotal evidence that visual estimation of the LVEF based on the area changes of the LV blood pool significantly underestimate true LVEF. True LVEF should be calculated based on changes in LV volumes between diastole and systole. In this project, we utilized both idealized models of the LV geometry - a truncated prolate spheroid (TPS) and a paraboloid model - to represent the LV anatomy. Cross-sectional areas and volumes of simulated LV shapes using both models were calculated to compare the LVEF. Further, a LV reconstruction algorithm was employed to build the LV blood pool volume in both systole and diastole from multi-plane 2D US imaging data. Our mathematical models yielded an area-based LVEF of 41 4.7% and a volume-based LVEF of 55 ±5.7%, while the 3D recon-struction model showed an area-based LVEF of 35 11.9% and a volume-based LVEF of 48.0 ± 14.0%. In summary, the area-based LVEF using all three models ±underestimate the volume-based LVEF using corresponding models by 13% to 14%. This preliminary study confirms both mathematically and empirically that area-based LVEF estimates indeed underestimate the true volume-based LVEF measurements and suggests that true volumetric measurements of the LV blood pool must be computed to correctly assess cardiac LVEF.</p>","PeriodicalId":92712,"journal":{"name":"Proceedings. IEEE Western New York Image and Signal Processing Workshop","volume":"2018 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/WNYIPW.2018.8576438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37354795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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