The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Paul L Carson - One of the best experts on this subject based on the ideXlab platform.
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medical breast Ultrasound Image segmentation by machine learning
Ultrasonics, 2019Co-Authors: Yuxin Wang, Jie Yuan, Xueding Wang, Qian Cheng, Paul L CarsonAbstract:Breast cancer is the most commonly diagnosed cancer, which alone accounts for 30% all new cancer diagnoses for women, posing a threat to women's health. Segmentation of breast Ultrasound Images into functional tissues can aid tumor localization, breast density measurement, and assessment of treatment response, which is important to the clinical diagnosis of breast cancer. However, manually segmenting the Ultrasound Images, which is skill and experience dependent, would lead to a subjective diagnosis; in addition, it is time-consuming for radiologists to review hundreds of clinical Images. Therefore, automatic segmentation of breast Ultrasound Images into functional tissues has received attention in recent years, amidst the more numerous studies of detection and segmentation of masses. In this paper, we propose to use convolutional neural networks (CNNs) for segmenting breast Ultrasound Images into four major tissues: skin, fibroglandular tissue, mass, and fatty tissue, on three-dimensional (3D) breast Ultrasound Images. Quantitative metrics for evaluation of segmentation results including Accuracy, Precision, Recall, and F1measure, all reached over 80%, which indicates that the method proposed has the capacity to distinguish functional tissues in breast Ultrasound Images. Another metric called the Jaccard similarity index (JSI) yields an 85.1% value, outperforming our previous study using the watershed algorithm with 74.54% JSI value. Thus, our proposed method might have the potential to provide the segmentations necessary to assist the clinical diagnosis of breast cancer and improve imaging in other modes in medical Ultrasound.
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automated 3d Ultrasound Image segmentation to aid breast cancer Image interpretation
Ultrasonics, 2016Co-Authors: Won Mean Lee, Marilyn A Roubidoux, Jie Yuan, Xueding Wang, Paul L CarsonAbstract:Segmentation of an Ultrasound Image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in Ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) Ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D Ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast Ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast Ultrasound volumes into functionally distinct tissues that may help to correct Ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.
Jussi Tohka - One of the best experts on this subject based on the ideXlab platform.
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evaluation and comparison of current fetal Ultrasound Image segmentation methods for biometric measurements a grand challenge
IEEE Transactions on Medical Imaging, 2014Co-Authors: Sylvia Rueda, Sana Fathima, C L Knight, M Yaqub, A T Papageorghiou, Bahbibi Rahmatullah, Matteo Maggioni, Antonietta Pepe, Jussi Tohka, Richard V StebbingAbstract:This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal Ultrasound Image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal Ultrasound Images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying Image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.
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evaluation and comparison of current fetal Ultrasound Image segmentation methods for biometric measurements a grand challenge
IEEE Transactions on Medical Imaging, 2014Co-Authors: Sylvia Rueda, Sana Fathima, C L Knight, M Yaqub, A T Papageorghiou, Bahbibi Rahmatullah, Matteo Maggioni, Antonietta Pepe, Alessandro Foi, Jussi TohkaAbstract:This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal Ultrasound Image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal Ultrasound Images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying Image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.
Sylvia Rueda - One of the best experts on this subject based on the ideXlab platform.
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evaluation and comparison of current fetal Ultrasound Image segmentation methods for biometric measurements a grand challenge
IEEE Transactions on Medical Imaging, 2014Co-Authors: Sylvia Rueda, Sana Fathima, C L Knight, M Yaqub, A T Papageorghiou, Bahbibi Rahmatullah, Matteo Maggioni, Antonietta Pepe, Jussi Tohka, Richard V StebbingAbstract:This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal Ultrasound Image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal Ultrasound Images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying Image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.
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evaluation and comparison of current fetal Ultrasound Image segmentation methods for biometric measurements a grand challenge
IEEE Transactions on Medical Imaging, 2014Co-Authors: Sylvia Rueda, Sana Fathima, C L Knight, M Yaqub, A T Papageorghiou, Bahbibi Rahmatullah, Matteo Maggioni, Antonietta Pepe, Alessandro Foi, Jussi TohkaAbstract:This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal Ultrasound Image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal Ultrasound Images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying Image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.
Robert D. Herbert - One of the best experts on this subject based on the ideXlab platform.
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effect of transducer orientation on errors in Ultrasound Image based measurements of human medial gastrocnemius muscle fascicle length and pennation
PLOS ONE, 2016Co-Authors: Robert D. Herbert, Bart Bolsterlee, Simon C. GandeviaAbstract:Ultrasound imaging is often used to measure muscle fascicle lengths and pennation angles in human muscles in vivo. Theoretically the most accurate measurements are made when the transducer is oriented so that the Image plane aligns with muscle fascicles and, for measurements of pennation, when the Image plane also intersects the aponeuroses perpendicularly. However this orientation is difficult to achieve and usually there is some degree of misalignment. Here, we used simulated Ultrasound Images based on three-dimensional models of the human medial gastrocnemius, derived from magnetic resonance and diffusion tensor Images, to describe the relationship between transducer orientation and measurement errors. With the transducer oriented perpendicular to the surface of the leg, the error in measurement of fascicle lengths was about 0.4 mm per degree of misalignment of the Ultrasound Image with the muscle fascicles. If the transducer is then tipped by 20°, the error increases to 1.1 mm per degree of misalignment. For a given degree of misalignment of muscle fascicles with the Image plane, the smallest absolute error in fascicle length measurements occurs when the transducer is held perpendicular to the surface of the leg. Misalignment of the transducer with the fascicles may cause fascicle length measurements to be underestimated or overestimated. Contrary to widely held beliefs, it is shown that pennation angles are always overestimated if the Image is not perpendicular to the aponeurosis, even when the Image is perfectly aligned with the fascicles. An analytical explanation is provided for this finding.
Richard V Stebbing - One of the best experts on this subject based on the ideXlab platform.
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evaluation and comparison of current fetal Ultrasound Image segmentation methods for biometric measurements a grand challenge
IEEE Transactions on Medical Imaging, 2014Co-Authors: Sylvia Rueda, Sana Fathima, C L Knight, M Yaqub, A T Papageorghiou, Bahbibi Rahmatullah, Matteo Maggioni, Antonietta Pepe, Jussi Tohka, Richard V StebbingAbstract:This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal Ultrasound Image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal Ultrasound Images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying Image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.
