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Yao Lu - One of the best experts on this subject based on the ideXlab platform.
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automatic segmentation of the Pectoral Muscle based on boundary identification and shape prediction
Physics in Medicine and Biology, 2020Co-Authors: Wenhui Zhao, Songfeng Li, Yaqin Zhang, Yao LuAbstract:The purpose of this work is to identify the Pectoral Muscle region in mediolateral oblique (MLO) view mammograms even when the boundary is blurred or obscured. The problem is decoupled into two subproblems in our study: identifying parts of boundaries with high confidence and predicting the overall shape of the Pectoral Muscle. Due to the similarity in intensity and texture between Pectoral Muscle and gland tissue, we trained a deep neural network to distinguish them in the first subproblem. The boundary with high confidence can be obtained according to the consistency of predictions from multiple converged models. For the shape prediction problem, a generative adversarial network (GAN) is used to learn mapping from a given identified region and the breast shape to the overall Pectoral Muscle shape. Our method is evaluated on a mammogram dataset including 633 MLO view mammograms collected from three different datacenters. We take U-Net as our baseline model and the dataset is divided into three groups according to the performance of U-Net for evaluation. In all three groups, U-Net achieves 80.1%, 92.9%, and 98.3% in the Dice similarity coefficient, respectively, and our method achieves 85.2%, 94.8%, and 98.1% in the Dice similarity coefficient, respectively. The experiment shows that our method effectively estimates the Pectoral Muscle boundary, even parts of boundaries that are difficult to detect, and greatly improves the performance of segmentation in this case.
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automated Pectoral Muscle identification on mlo view mammograms comparison of deep neural network to conventional computer vision
Medical Physics, 2019Co-Authors: Chuan Zhou, Heangping Chan, Lubomir M Hadjiiski, Mark A Helvie, Yao LuAbstract:OBJECTIVES: The aim of this study was to develop a fully automated deep learning approach for identification of the Pectoral Muscle on mediolateral oblique (MLO) view mammograms and evaluate its performance in comparison to our previously developed texture-field orientation (TFO) method using conventional image feature analysis. Pectoral Muscle segmentation is an important step for automated image analyses such as breast density or parenchymal pattern classification, lesion detection, and multiview correlation. MATERIALS AND METHODS: Institutional Review Board (IRB) approval was obtained before data collection. A dataset of 729 MLO-view mammograms including 637 digitized film mammograms (DFM) and 92 digital mammograms (DM) from our previous study was used for the training and validation of our deep convolutional neural network (DCNN) segmentation method. In addition, we collected an independent set of 203 DMs from 131 patients for testing. The film mammograms were digitized at a pixel size of 50 μm × 50 μm with a Lumiscan digitizer. All DMs were acquired with GE systems at a pixel size of 100 μm × 100 μm. An experienced MQSA radiologist manually drew the Pectoral Muscle boundary on each mammogram as the reference standard. We trained the DCNN to estimate a probability map of the Pectoral Muscle region on mammograms. The DCNN consisted of a contracting path to capture multiresolution image context and a symmetric expanding path for prediction of the Pectoral Muscle region. Three DCNN structures were compared for automated identification of Pectoral Muscles. Tenfold cross-validation was used in training of the DCNNs. After training, we applied the ten trained models during cross-validation to the independent DM test set. The predicted Pectoral Muscle region of each test DM was obtained as the mean probability map by averaging the ensemble of probability maps from the ten models. The DCNN-segmented Pectoral Muscle was evaluated by three performance measures relative to the reference standard: (a) the percent overlap area (POA) of the Pectoral Muscle regions, (b) the Hausdorff distance (Hdist), and (c) the average Euclidean distance (AvgDist) between the boundaries. The results were compared to those obtained with the TFO method, used as our baseline. A two-tailed paired t test was performed to examine the significance in the differences between the DCNN and the baseline. RESULTS: In the ten test partitions of the cross-validation set, the DCNN achieved a mean POA of 96.5 ± 2.9%, a mean Hdist of 2.26 ± 1.31 mm, and a mean AvgDist of 0.78 ± 0.58 mm, while the corresponding measures by the baseline method were 94.2 ± 4.8%, 3.69 ± 2.48 mm, and 1.30 ± 1.22 mm, respectively. For the independent DM test set, the DCNN achieved a mean POA of 93.7% ± 6.9%, a mean Hdist of 3.80 ± 3.21 mm, and a mean AvgDist of 1.49 ± 1.62 mm comparing to 86.9% ± 16.0%, 7.18 ± 14.22 mm, and 3.98 ± 14.13 mm, respectively, by the baseline method. CONCLUSION: In comparison to the TFO method, DCNN significantly improved the accuracy of Pectoral Muscle identification on mammograms (P < 0.05).
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fully automated Pectoral Muscle identification on mlo view mammograms with deep convolutional neural network
14th International Workshop on Breast Imaging (IWBI 2018), 2018Co-Authors: Chuan Zhou, Heangping Chan, Lubomir Hadjiyski, Yao LuAbstract:Accurate segmentation of breast region is an essential step for quantitative analysis of breast parenchyma on mammograms. Pectoral Muscle identification on mediolateral oblique (MLO) view mammograms remains a challenging problem. In this study, our purpose is to develop a supervised deep learning approach for automated identification of the Pectoral Muscle on MLO-view mammograms. With IRB approval, 756 MLO-view mammograms including 656 digitized film mammograms (DFM) and 100 full field digital mammograms (DM) were retrospectively collected. The film mammograms were digitized at a pixel size of 50 μm × 50 μm and the DMs were acquired with a GE Senographe system with a pixel size of 100 μm × 100 μm. All mammograms were subsampled to 800 μm × 800 μm before the Pectoral Muscle analysis. An experienced radiologist manually segmented the Pectoral Muscle boundary as the reference standard. We constructed a U-Net-like deep convolutional neural network (DCNN) to identify the boundary of the Pectoral Muscle. The DCNN consisted of a contracting path to capture multi-resolution image context and a symmetric expanding path for prediction of the Pectoral Muscle region. A total of 15 million parameters in DCNN were trained with a mini-batched gradient decent algorithm by minimizing a binary cross-entropy cost function. Ten-fold crossvalidation was used in training and evaluating the performance of our model. The DCNN-segmented Pectoral Muscle was compared to the reference standard with three criteria: 1) the percent overlap area (POA), 2) the Hausdorff distance (Hdist) and 3) the average Euclidean distance (AvgDist). We found that the mean POA, the mean Hdist, and the mean AvgDist were 96.0±5.3%, 2.14±1.50 mm, and 0.77± 0.97 mm, respectively. Further study is underway to evaluate its effect on quantitative analysis of mammograms.
Theunis Piersma - One of the best experts on this subject based on the ideXlab platform.
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evolutionary design of a flexible seasonally migratory avian phenotype why trade gizzard mass against Pectoral Muscle mass
Proceedings of The Royal Society B: Biological Sciences, 2019Co-Authors: Anne Dekinga, Theunis Piersma, Kimberley J Mathot, Joseph B Burant, Petra Manche, Darren SaintongeAbstract:Migratory birds undergo impressive body remodelling over the course of an annual cycle. Prior to long-distance flights, red knots ( Calidris canutus islandica) reduce gizzard mass while increasing body mass and Pectoral Muscle mass. Although body mass and Pectoral Muscle mass are functionally linked via their joint effects on flight performance, gizzard and Pectoral Muscle mass are thought to be independently regulated. Current hypotheses for observed negative within-individual covariation between gizzard and Pectoral Muscle mass in free-living knots are based on a common factor (e.g. migration) simultaneously affecting both traits, and/or protein limitation forcing allocation decisions. We used diet manipulations to generate within-individual variation in gizzard mass and test for independence between gizzard and Pectoral Muscle mass within individuals outside the period of migration and under conditions of high protein availability. Contrary to our prediction, we observed a negative within-individual covariation between gizzard and Pectoral Muscle mass. We discuss this result as a potential outcome of an evolved mechanism underlying body remodelling associated with migration. Although our proposed mechanism requires empirical testing, this study echoes earlier calls for greater integration of studies of function and mechanism, and in particular, the need for more explicit consideration of the evolution of mechanisms underlying phenotypic design.
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avian Pectoral Muscle size rapidly tracks body mass changes during flight fasting and fuelling
The Journal of Experimental Biology, 2000Co-Authors: Ake Lindstrom, Anne Dekinga, Theunis Piersma, Anders Kvist, Maurine W DietzAbstract:We used ultrasonic imaging to monitor short-term changes in the Pectoral Muscle size of captive red knots Calidris canutus. Pectoral Muscle thickness changed rapidly and consistently in parallel with body mass changes caused by flight, fasting and fuelling. Four knots flew repeatedly for 10 h periods in a wind tunnel. Over this period, Pectoral Muscle thickness decreased in parallel with the decrease in body mass. The change in Pectoral Muscle thickness during flight was indistinguishable from that during periods of natural and experimental fasting and fuelling. The body-mass-related variation in Pectoral Muscle thickness between and within individuals was not related to the amount of flight, indicating that changes in avian Muscle do not require power-training as in mammals. Our study suggests that it is possible for birds to consume and replace their flight Muscles on a time scale short enough to allow these Muscles to be used as part of the energy supply for migratory flight. The adaptive significance of the changes in Pectoral Muscle mass cannot be explained by reproductive needs since our knots were in the early winter phase of their annual cycle. Instead, Pectoral Muscle mass changes may reflect (i) the breakdown of protein during heavy exercise and its subsequent restoration, (ii) the regulation of flight capacity to maintain optimal flight performance when body mass varies, or (iii) the need for a particular protein:fat ratio in winter survival stores.
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Body-building without power training: Endogenously regulated Pectoral Muscle hypertrophy in confined shorebirds
Journal of Experimental Biology, 1999Co-Authors: M W Dietz, Theunis Piersma, Anne DekingaAbstract:Shorebirds such as red knots Calidris canutus routinely make migratory flights of 3000 km or more. Previous studies on this species, based on compositional analyses, suggest extensive Pectoral Muscle hypertrophy in addition to fat storage before take-off. Such hypertrophy could be due to power training and/or be effected by an endogenous circannual rhythm. Red knots of two subspecies with contrasting migration patterns were placed in a climate-controlled aviary (12 h:12 h L:D photoperiod) where exercise was limited. Using ultrasonography, we measured Pectoral Muscle size as the birds stored fat in preparation for migration. At capture, there were no differences in body mass and Pectoral Muscle mass between the two subspecies. As they prepared for southward and northward migration, respectively, the tropically wintering subspecies (C. c. canutus) gained 31 g and the temperate wintering subspecies (C. c. islandica) gained 41 g. During this time, Pectoral mass increased by 43-44% of initial mass, representing 39% (C. c. canutus) and 29% (C. c. islandica) of the increase in body mass. The gizzard showed atrophy in conjunction with a diet change from molluscs to food pellets. Although we cannot exclude the possibility that the birds' limited movement may still be a prerequisite for Pectoral Muscle hypertrophy, extensive power training is certainly not a requirement. Muscle hypertrophy in the absence of photoperiod cues suggests the involvement of an endogenous circannual process.
Andrik Rampun - One of the best experts on this subject based on the ideXlab platform.
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breast Pectoral Muscle segmentation in mammograms using a modified holistically nested edge detection network
Medical Image Analysis, 2019Co-Authors: Andrik Rampun, Philip Morrow, Bryan Scotney, Karen Lopezlinares, Hui Wang, Inmaculada Garcia Ocana, Gregory Maclair, Reyer Zwiggelaar, Miguel Angel Gonzalez BallesterAbstract:Abstract This paper presents a method for automatic breast Pectoral Muscle segmentation in mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired by the Holistically-nested Edge Detection (HED) network. Most of the existing methods in the literature are based on hand-crafted models such as straight-line, curve-based techniques or a combination of both. Unfortunately, such models are insufficient when dealing with complex shape variations of the Pectoral Muscle boundary and when the boundary is unclear due to overlapping breast tissue. To compensate for these issues, we propose a neural network framework that incorporates multi-scale and multi-level learning, capable of learning complex hierarchical features to resolve spatial ambiguity in estimating the Pectoral Muscle boundary. For this purpose, we modified the HED network architecture to specifically find ‘contour-like’ objects in mammograms. The proposed framework produced a probability map that can be used to estimate the initial Pectoral Muscle boundary. Subsequently, we process these maps by extracting morphological properties to find the actual Pectoral Muscle boundary. Finally, we developed two different post-processing steps to find the actual Pectoral Muscle boundary. Quantitative evaluation results show that the proposed method is comparable with alternative state-of-the-art methods producing on average values of 94.8 ± 8.5% and 97.5 ± 6.3% for the Jaccard and Dice similarity metrics, respectively, across four different databases.
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fully automated breast boundary and Pectoral Muscle segmentation in mammograms
Artificial Intelligence in Medicine, 2017Co-Authors: Andrik Rampun, Philip Morrow, Bryan Scotney, John WinderAbstract:Abstract Breast and Pectoral Muscle segmentation is an essential pre-processing step for the subsequent processes in computer aided diagnosis (CAD) systems. Estimating the breast and Pectoral boundaries is a difficult task especially in mammograms due to artifacts, homogeneity between the Pectoral and breast regions, and low contrast along the skin-air boundary. In this paper, a breast boundary and Pectoral Muscle segmentation method in mammograms is proposed. For breast boundary estimation, we determine the initial breast boundary via thresholding and employ Active Contour Models without edges to search for the actual boundary. A post-processing technique is proposed to correct the overestimated boundary caused by artifacts. The Pectoral Muscle boundary is estimated using Canny edge detection and a pre-processing technique is proposed to remove noisy edges. Subsequently, we identify five edge features to find the edge that has the highest probability of being the initial Pectoral contour and search for the actual boundary via contour growing. The segmentation results for the proposed method are compared with manual segmentations using 322, 208 and 100 mammograms from the Mammographic Image Analysis Society (MIAS), INBreast and Breast Cancer Digital Repository (BCDR) databases, respectively. Experimental results show that the breast boundary and Pectoral Muscle estimation methods achieved dice similarity coefficients of 98.8% and 97.8% (MIAS), 98.9% and 89.6% (INBreast) and 99.2% and 91.9% (BCDR), respectively.
Mislav Grgic - One of the best experts on this subject based on the ideXlab platform.
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BREAST BORDER EXTRACTION AND Pectoral Muscle DETECTION
2020Co-Authors: Mario Mustra, Jelena Bozek, Mislav GrgicAbstract:Digital mammography is used more and more each day in comparison with screen film mammography (SFM). Main advantage of digital mammography for image processing is the use of images with few or no artifacts that can occur on SFM images. Finding breast border contour is therefore easier and gives more precise results. On the other hand, detection of Pectoral Muscle and breast abnormalities has almost the same results in both cases. The presence of Pectoral Muscle can affect results of lesion detection algorithms so it is recommended to have it removed from the image. Detection and segmentation of Pectoral Muscle can also help in image registration for further analysis of breast abnormalities such as bilateral asymmetry. Algorithm presented in this paper uses hybrid method for the Pectoral Muscle detection. Proposed method uses bit depth reduction and wavelet decomposition for finding Pectoral Muscle border. Algorithm has been tested on the set of 40 digital mammography images.
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robust automatic breast and Pectoral Muscle segmentation from scanned mammograms
Signal Processing, 2013Co-Authors: Mario Mustra, Mislav GrgicAbstract:Breast skin–air interface and Pectoral Muscle segmentation are usually first steps in all CAD applications on scanned as well as digital mammograms. Breast skin–air interface segmentation is much more difficult task when performed on scanned mammograms than on digital mammograms. In case of Pectoral Muscle segmentation, segmentation difficulty of analog and digital mammograms is usually similar. In this paper we present adaptive contrast enhancement method for breast skin–air interface detection which combines usage of adaptive histogram equalization method on small region of interest which contains actual edge and edge detection operators. Pectoral Muscle detection method uses combination of contrast enhancement using adaptive histogram equalization and polynomial curvature estimation on selected region of interest. This method makes segmentation of very low contrast Pectoral Muscle areas possible because of estimation used to segment areas which have lower contrast difference than detection threshold.
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breast border extraction and Pectoral Muscle detection using wavelet decomposition
IEEE EUROCON, 2009Co-Authors: Mario Mustra, Jelena Bozek, Mislav GrgicAbstract:Digital mammography is used more and more each day in comparison with screen film mammography (SFM). Main advantage of digital mammography for image processing is the use of images with few or no artifacts that can occur on SFM images. Finding breast border contour is therefore easier and gives more precise results. On the other hand, detection of Pectoral Muscle and breast abnormalities has almost the same results in both cases. The presence of Pectoral Muscle can affect results of lesion detection algorithms so it is recommended to have it removed from the image. Detection and segmentation of Pectoral Muscle can also help in image registration for further analysis of breast abnormalities such as bilateral asymmetry. Algorithm presented in this paper uses hybrid method for the Pectoral Muscle detection. Proposed method uses bit depth reduction and wavelet decomposition for finding Pectoral Muscle border. Algorithm has been tested on the set of 40 digital mammography images.
Kimberley J Mathot - One of the best experts on this subject based on the ideXlab platform.
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evolutionary design of a flexible seasonally migratory avian phenotype why trade gizzard mass against Pectoral Muscle mass
Proceedings of The Royal Society B: Biological Sciences, 2019Co-Authors: Anne Dekinga, Theunis Piersma, Kimberley J Mathot, Joseph B Burant, Petra Manche, Darren SaintongeAbstract:Migratory birds undergo impressive body remodelling over the course of an annual cycle. Prior to long-distance flights, red knots ( Calidris canutus islandica) reduce gizzard mass while increasing body mass and Pectoral Muscle mass. Although body mass and Pectoral Muscle mass are functionally linked via their joint effects on flight performance, gizzard and Pectoral Muscle mass are thought to be independently regulated. Current hypotheses for observed negative within-individual covariation between gizzard and Pectoral Muscle mass in free-living knots are based on a common factor (e.g. migration) simultaneously affecting both traits, and/or protein limitation forcing allocation decisions. We used diet manipulations to generate within-individual variation in gizzard mass and test for independence between gizzard and Pectoral Muscle mass within individuals outside the period of migration and under conditions of high protein availability. Contrary to our prediction, we observed a negative within-individual covariation between gizzard and Pectoral Muscle mass. We discuss this result as a potential outcome of an evolved mechanism underlying body remodelling associated with migration. Although our proposed mechanism requires empirical testing, this study echoes earlier calls for greater integration of studies of function and mechanism, and in particular, the need for more explicit consideration of the evolution of mechanisms underlying phenotypic design.