Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)最新文献
Mika Yamamuro, Y. Asai, Naomi Hashimoto, Nao Yasuda, Hiroto Kimura, Takahiro Yamada, M. Nemoto, Yuichi Kimura, H. Handa, Hisashi Yoshida, K. Abe, M. Tada, H. Habe, T. Nagaoka, Seiun Nin, Kazunari Ishii, Yongbum Lee
Many studies have assessed breast density in clinical practice. However, calculation of breast density requires segmentation of the mammary gland region, and deep learning has only recently been applied. Thus, the robustness of the deep learning model for different image processing types has not yet been reported. We investigated the accuracy of segmentation of the U-net for mammograms made with variousimage processing types. We used 478 mediolateral oblique view mammograms. The mammograms were divided into 390 training images and 88 testing images. The ground truth of the mammary gland region made by mammary experts was used for the training and testing datasets. Four types of image processing (Types 1–4) were applied to the testing images to compare breast density in the segmented mammary gland regions with that of ground truths. The shape agreement between ground truth and the segmented mammary gland region by U-net of Types 1–4 was assessed using the Dice coefficient, and the equivalence or compatibility of breast density with ground truth was assessed by Bland-Altman analysis. The mean Dice coefficients between the ground truth and U-net were 0.952, 0.948, 0.948, and 0.947 for Types 1, 2, 3, and 4, respectively. By Bland-Altman analysis, the equivalence of breast density between ground truth and U-net was confirmed for Types 1 and 2, and compatibility was confirmed for Types 3 and 4. We concluded that the robustness of the U-net for segmenting the mammary gland region was confirmed for different image processing types.
{"title":"Robustness of a U-net model for different image processing types in segmentation of the mammary gland region","authors":"Mika Yamamuro, Y. Asai, Naomi Hashimoto, Nao Yasuda, Hiroto Kimura, Takahiro Yamada, M. Nemoto, Yuichi Kimura, H. Handa, Hisashi Yoshida, K. Abe, M. Tada, H. Habe, T. Nagaoka, Seiun Nin, Kazunari Ishii, Yongbum Lee","doi":"10.1117/12.2624139","DOIUrl":"https://doi.org/10.1117/12.2624139","url":null,"abstract":"Many studies have assessed breast density in clinical practice. However, calculation of breast density requires segmentation of the mammary gland region, and deep learning has only recently been applied. Thus, the robustness of the deep learning model for different image processing types has not yet been reported. We investigated the accuracy of segmentation of the U-net for mammograms made with variousimage processing types. We used 478 mediolateral oblique view mammograms. The mammograms were divided into 390 training images and 88 testing images. The ground truth of the mammary gland region made by mammary experts was used for the training and testing datasets. Four types of image processing (Types 1–4) were applied to the testing images to compare breast density in the segmented mammary gland regions with that of ground truths. The shape agreement between ground truth and the segmented mammary gland region by U-net of Types 1–4 was assessed using the Dice coefficient, and the equivalence or compatibility of breast density with ground truth was assessed by Bland-Altman analysis. The mean Dice coefficients between the ground truth and U-net were 0.952, 0.948, 0.948, and 0.947 for Types 1, 2, 3, and 4, respectively. By Bland-Altman analysis, the equivalence of breast density between ground truth and U-net was confirmed for Types 1 and 2, and compatibility was confirmed for Types 3 and 4. We concluded that the robustness of the U-net for segmenting the mammary gland region was confirmed for different image processing types.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"8 2","pages":"122860T - 122860T-6"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72596274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. B. Vimieiro, L. Borges, Renato F Caron, B. Barufaldi, Andrew D. A. Maidment, Ge Wang, M. Vieira
It is well-known that x-ray systems featuring indirect detectors are affected by noise spatial correlation. In the case of digital breast tomosynthesis (DBT), this phenomenon might affect the perception of small details in the image, such as microcalcifications. In this work, we propose the use of a deep convolutional neural network (CNN) to restore DBT projections degraded with correlated noise using the framework of a cycle generative adversarial network (cycle-GAN). To generate pairs of images for the training procedure, we used a virtual clinical trial (VCT) system. Two approaches were evaluated: in the first one, the network was trained to perform noise decorrelation by changing the frequency-dependency of the noise in the input image, but keeping the other characteristics. In the second approach, the network was trained to perform denoising and decorrelation, with the objective of generating an image with frequency-independent (white) noise and with characteristics equivalent to an acquisition with a radiation exposure four times greater than the input image. We tested the network with virtual and clinical images and we found that in both training approaches the model successfully corrected the power spectrum of the input images.
{"title":"Suppressing noise correlation in digital breast tomosynthesis using convolutional neural network and virtual clinical trials","authors":"R. B. Vimieiro, L. Borges, Renato F Caron, B. Barufaldi, Andrew D. A. Maidment, Ge Wang, M. Vieira","doi":"10.1117/12.2625357","DOIUrl":"https://doi.org/10.1117/12.2625357","url":null,"abstract":"It is well-known that x-ray systems featuring indirect detectors are affected by noise spatial correlation. In the case of digital breast tomosynthesis (DBT), this phenomenon might affect the perception of small details in the image, such as microcalcifications. In this work, we propose the use of a deep convolutional neural network (CNN) to restore DBT projections degraded with correlated noise using the framework of a cycle generative adversarial network (cycle-GAN). To generate pairs of images for the training procedure, we used a virtual clinical trial (VCT) system. Two approaches were evaluated: in the first one, the network was trained to perform noise decorrelation by changing the frequency-dependency of the noise in the input image, but keeping the other characteristics. In the second approach, the network was trained to perform denoising and decorrelation, with the objective of generating an image with frequency-independent (white) noise and with characteristics equivalent to an acquisition with a radiation exposure four times greater than the input image. We tested the network with virtual and clinical images and we found that in both training approaches the model successfully corrected the power spectrum of the input images.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"26 1","pages":"122861B - 122861B-7"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78994212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fuyu Harada, Y. Uchiyama, Kie Shimizu, Yutaka Yamamoto
The effectiveness of pharmacotherapy has been improved through the development of drugs that incorporate the knowledge of the molecular biology of breast cancer. Therefore, neoadjuvant chemotherapy (NAC) is actively administered to patients who wish to undergo breast-conserving surgery. During NAC, some patients have a pathological complete response (pCR). This study aims to develop a method for predicting patients with pCR during NAC. This creates new value for preoperative imaging. Breast ultrasound images were collected from 43 patients with breast cancer who received NAC at the Kumamoto University Hospital. The tumor area on the breast ultrasound image was manually marked. From the marked tumor regions, 379 radiomics features related to size, shape, density, and texture were measured. We employed the least absolute shrinkage and selection operator to select the useful radiomic features. Linear discriminant analysis (LDA) with eight selected radiomic features was used to distinguish between pCR and non-pCR. Leave-one-out was used for training and testing LDA. The sensitivity, specificity, and AUC were 89.5 % (17/19), 83.3% (19/24), and 0.920, respectively. Because the LDA is the simplest classifier, the phenotype of the lesion in the breast ultrasound image may contain information that predicts the therapeutic effect. Our proposed method could provide a new value for preoperative imaging.
{"title":"Prediction of pathological complete response with neoadjuvant chemotherapy by using radiomic features in breast ultrasound image","authors":"Fuyu Harada, Y. Uchiyama, Kie Shimizu, Yutaka Yamamoto","doi":"10.1117/12.2623991","DOIUrl":"https://doi.org/10.1117/12.2623991","url":null,"abstract":"The effectiveness of pharmacotherapy has been improved through the development of drugs that incorporate the knowledge of the molecular biology of breast cancer. Therefore, neoadjuvant chemotherapy (NAC) is actively administered to patients who wish to undergo breast-conserving surgery. During NAC, some patients have a pathological complete response (pCR). This study aims to develop a method for predicting patients with pCR during NAC. This creates new value for preoperative imaging. Breast ultrasound images were collected from 43 patients with breast cancer who received NAC at the Kumamoto University Hospital. The tumor area on the breast ultrasound image was manually marked. From the marked tumor regions, 379 radiomics features related to size, shape, density, and texture were measured. We employed the least absolute shrinkage and selection operator to select the useful radiomic features. Linear discriminant analysis (LDA) with eight selected radiomic features was used to distinguish between pCR and non-pCR. Leave-one-out was used for training and testing LDA. The sensitivity, specificity, and AUC were 89.5 % (17/19), 83.3% (19/24), and 0.920, respectively. Because the LDA is the simplest classifier, the phenotype of the lesion in the breast ultrasound image may contain information that predicts the therapeutic effect. Our proposed method could provide a new value for preoperative imaging.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"200 1","pages":"122860S - 122860S-6"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79525320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As deep learning models are increasingly applied in medical diagnostic assistance systems, this raises questions about ones ability to understand and interpret its decision-making process. In this work, using breast lesions from the Optimam Mammography Image Database (OMI-DB), we have explored whether deep learned features have similar predictive information as classical texture features. We trained a deep learning model for mass lesion classification and used Gradient-weighted Class Activation Mapping to produce a representation of deep learned features. Additional, classical texture features (e.g. energy) were extracted. Subsequently, we used the earth mover’s distance to investigate similarities between deep learned and texture features. The comparison identified that texture features such as mean, entropy and auto-correlation showed a strong similarity with the deep learned features and provided an indication of what the deep learning models might have used as information for its classification.
{"title":"Comparison of deep learned and texture features in mammographic mass classification","authors":"Guobin Li, Cory Thomas, R. Zwiggelaar","doi":"10.1117/12.2625774","DOIUrl":"https://doi.org/10.1117/12.2625774","url":null,"abstract":"As deep learning models are increasingly applied in medical diagnostic assistance systems, this raises questions about ones ability to understand and interpret its decision-making process. In this work, using breast lesions from the Optimam Mammography Image Database (OMI-DB), we have explored whether deep learned features have similar predictive information as classical texture features. We trained a deep learning model for mass lesion classification and used Gradient-weighted Class Activation Mapping to produce a representation of deep learned features. Additional, classical texture features (e.g. energy) were extracted. Subsequently, we used the earth mover’s distance to investigate similarities between deep learned and texture features. The comparison identified that texture features such as mean, entropy and auto-correlation showed a strong similarity with the deep learned features and provided an indication of what the deep learning models might have used as information for its classification.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"2013 1","pages":"122860N - 122860N-7"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86221674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Måns Boll, T. Vent, Hanna Tomic, C. Bernhardsson, M. Dustler, A. Tingberg, P. Bakic
Objects created by 3D printers are increasingly used in various medical applications. Today, affordable 3D printers, using Fused Deposition Modeling are widely available. In this project, a commercially available 3D printer was used to replicate a conventional radiographic contrast detail phantom. Printing materials were selected by comparing their x-ray attenuation properties. Two replicas were printed using polylactic acid, with different filling patterns. The printed phantoms were imaged by a clinical mammography system, using automatic exposure control. Phantom images were visually and quantitively compared to images of the corresponding conventional contrast detail phantom. Visual scoring of the contrast detail elements was performed by a medical physics student. Contrast-to-noise ratio (CNR) was calculated for each phantom element. The diameter and thickness of the smallest visible phantom object were 0.44 mm and 0.09 mm, respectively, for both filling patterns. For the conventional phantom, the diameter and thickness of the smallest visible object were 0.31 mm and 0.09 mm. Visual inspection of printed phantoms revealed some linear artefacts. These artefacts were however not visible on mammographic projections. Quantitively, average CNR of printed phantom objects followed the same trend with an increase of average CNR with increasing disk height. However, there is a limitation of detail objects with disk diameters below 1.25 mm, caused by the available nozzle size. Based upon the encouraging results, future work will explore the use of different materials and smaller nozzle diameters.
{"title":"Evaluation of 3D printed contrast detail phantoms for mammography quality assurance","authors":"Måns Boll, T. Vent, Hanna Tomic, C. Bernhardsson, M. Dustler, A. Tingberg, P. Bakic","doi":"10.1117/12.2625732","DOIUrl":"https://doi.org/10.1117/12.2625732","url":null,"abstract":"Objects created by 3D printers are increasingly used in various medical applications. Today, affordable 3D printers, using Fused Deposition Modeling are widely available. In this project, a commercially available 3D printer was used to replicate a conventional radiographic contrast detail phantom. Printing materials were selected by comparing their x-ray attenuation properties. Two replicas were printed using polylactic acid, with different filling patterns. The printed phantoms were imaged by a clinical mammography system, using automatic exposure control. Phantom images were visually and quantitively compared to images of the corresponding conventional contrast detail phantom. Visual scoring of the contrast detail elements was performed by a medical physics student. Contrast-to-noise ratio (CNR) was calculated for each phantom element. The diameter and thickness of the smallest visible phantom object were 0.44 mm and 0.09 mm, respectively, for both filling patterns. For the conventional phantom, the diameter and thickness of the smallest visible object were 0.31 mm and 0.09 mm. Visual inspection of printed phantoms revealed some linear artefacts. These artefacts were however not visible on mammographic projections. Quantitively, average CNR of printed phantom objects followed the same trend with an increase of average CNR with increasing disk height. However, there is a limitation of detail objects with disk diameters below 1.25 mm, caused by the available nozzle size. Based upon the encouraging results, future work will explore the use of different materials and smaller nozzle diameters.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"203 1","pages":"122860J - 122860J-10"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89318036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Wang, Ž. Klaneček, T. Wagner, L. Cockmartin, N. Marshall, A. Studen, R. Jeraj, H. Bosmans
Aim: This project is part of a long-term goal to apply radiomics-based risk prediction models designed for twodimensional (2D) digital mammography (DM) to three-dimensional (3D) digital breast tomosynthesis (DBT), using either the DBT projection views (PV) or the reconstructed planes. In this work, 2 fundamental aspects related to PVs were explored: (1) finding robust radiomic features for both DM and PV, and (2) selecting robust and informative radiomic features for both 2D and 3D modalities by requiring respectively invariance and noninvariance of these features across DBT projections. Methods: DM and PVs from combined DM and DBT acquisitions of phantom and patients were used in this study. Robust radiomic features in these images were identified by the intra-class correlation coefficient (ICC) between DM and the central PV for DBT. Then, projection invariant and noninvariant radiomic features of PVs for different projection angles were also characterized by ICC. Finally, selected projection invariant features of PVs were applied on a DM breast density classifier and their predictive power was compared to the results of DM. Results: A total of 70 out of 93 extracted radiomic features (75%) showed at least moderate reliability (ICC>0.5) between DM and the central PV. In addition, a decrease of feature reliability along increasing angular range was observed on both real and simulated datasets. With projection angle invariance as the feature selection method, overfitting of a DM density classifier was reduced. Conclusions: A large portion of radiomic features was robust between DM and the central PV without specific harmonization, suggesting that some parts of the radiomic features of DM can be applied to the DBT projection dataset. Additionally, 3D DBT could also benefit 2D DM through the projection angle variation test. Projectioninvariant features with better robustness could be selected for 2D DM which was preliminary validated by a density classification task, while projection non-invariant features which incorporate 3D information in the PVs may be suitable for 3D DBT.
{"title":"Applying radiomics-based risk prediction models from digital mammography to digital breast tomosynthesis: a preliminary reliability survey","authors":"Y. Wang, Ž. Klaneček, T. Wagner, L. Cockmartin, N. Marshall, A. Studen, R. Jeraj, H. Bosmans","doi":"10.1117/12.2624599","DOIUrl":"https://doi.org/10.1117/12.2624599","url":null,"abstract":"Aim: This project is part of a long-term goal to apply radiomics-based risk prediction models designed for twodimensional (2D) digital mammography (DM) to three-dimensional (3D) digital breast tomosynthesis (DBT), using either the DBT projection views (PV) or the reconstructed planes. In this work, 2 fundamental aspects related to PVs were explored: (1) finding robust radiomic features for both DM and PV, and (2) selecting robust and informative radiomic features for both 2D and 3D modalities by requiring respectively invariance and noninvariance of these features across DBT projections. Methods: DM and PVs from combined DM and DBT acquisitions of phantom and patients were used in this study. Robust radiomic features in these images were identified by the intra-class correlation coefficient (ICC) between DM and the central PV for DBT. Then, projection invariant and noninvariant radiomic features of PVs for different projection angles were also characterized by ICC. Finally, selected projection invariant features of PVs were applied on a DM breast density classifier and their predictive power was compared to the results of DM. Results: A total of 70 out of 93 extracted radiomic features (75%) showed at least moderate reliability (ICC>0.5) between DM and the central PV. In addition, a decrease of feature reliability along increasing angular range was observed on both real and simulated datasets. With projection angle invariance as the feature selection method, overfitting of a DM density classifier was reduced. Conclusions: A large portion of radiomic features was robust between DM and the central PV without specific harmonization, suggesting that some parts of the radiomic features of DM can be applied to the DBT projection dataset. Additionally, 3D DBT could also benefit 2D DM through the projection angle variation test. Projectioninvariant features with better robustness could be selected for 2D DM which was preliminary validated by a density classification task, while projection non-invariant features which incorporate 3D information in the PVs may be suitable for 3D DBT.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"26 1","pages":"1228614 - 1228614-10"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83734025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vincent Dong, Tristan D. Maidment, L. Borges, Katherine Hopkins, Johnny Kuo, Albert Milani, Peter Ringer, S. Ng
Digital mammography (DM) and digital breast tomosynthesis, the gold standards for breast cancer screening, requires correct breast positioning to ensure accuracy. Improper positioning can result in missed cancers, or can lead to additional imaging. We propose an automated deep learning (DL) segmentation approach to perform multi-class identification of regions of interest (ROI) commonly used for identification of poor positioning in mediolateral oblique (MLO) breast views. We hypothesize that by leveraging the capabilities of DL through the use of the well-founded U-Net model architecture, multi-class DL-based segmentation approaches can accurately identify air, parenchyma, pectoralis, and nipple locations within MLO images. In this study, we employed model hyperparameter searches to determine optimal model parameters for our proposed DL architecture, including the optimal loss function configuration; our best model achieved an average Sørensen-Dice coefficient of 0.919 ± 0.061 on the held-out test set. We identified high levels of localization performance in the nipple ROI. We believe our proposed segmentation model can be a foundational step in further mammogram analysis, such as for breast positioning and localized image processing tools.
{"title":"Automated multi-class segmentation of digital mammograms with deep convolutional neural networks","authors":"Vincent Dong, Tristan D. Maidment, L. Borges, Katherine Hopkins, Johnny Kuo, Albert Milani, Peter Ringer, S. Ng","doi":"10.1117/12.2626624","DOIUrl":"https://doi.org/10.1117/12.2626624","url":null,"abstract":"Digital mammography (DM) and digital breast tomosynthesis, the gold standards for breast cancer screening, requires correct breast positioning to ensure accuracy. Improper positioning can result in missed cancers, or can lead to additional imaging. We propose an automated deep learning (DL) segmentation approach to perform multi-class identification of regions of interest (ROI) commonly used for identification of poor positioning in mediolateral oblique (MLO) breast views. We hypothesize that by leveraging the capabilities of DL through the use of the well-founded U-Net model architecture, multi-class DL-based segmentation approaches can accurately identify air, parenchyma, pectoralis, and nipple locations within MLO images. In this study, we employed model hyperparameter searches to determine optimal model parameters for our proposed DL architecture, including the optimal loss function configuration; our best model achieved an average Sørensen-Dice coefficient of 0.919 ± 0.061 on the held-out test set. We identified high levels of localization performance in the nipple ROI. We believe our proposed segmentation model can be a foundational step in further mammogram analysis, such as for breast positioning and localized image processing tools.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"6 1","pages":"122860M - 122860M-7"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80380236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modelling of the breast surface shape under compression in the cranio-caudal and medio-lateral oblique views could advance the development of image processing techniques and of dosimetric estimates in digital mammography and digital breast tomosynthesis. Our goal is to compare the performance of a previously tested and used optical structured light scanning system (SLSS) capable of capturing the breast shape under compression to that of an infrared smartphone-based SLSS. Their performance was compared by scanning a cuboid phantom and two breast shaped phantoms (30 mm and 74 mm thick). Ten scans of the cuboid phantom were acquired with each scanner, and the measured length and thickness of the scanned shape were compared against the ground truth and between the two scanners. The performance of the scanners regarding breast-like phantoms was evaluated by calculating the maximum and mean distance, along with the root mean square difference, between each scanners result and against the matching ground truth. The cuboid phantom analysis showed a statistical difference for the thickness measurement in both scanners and in the length measurement for the optical scanner (p<0.05). However, no statistical difference was found between the scanner measurements. For the breast-like phantoms, the higher maximum distances were found in the infrared scans, but the mean distance between ground truth surface and the scans showed equivalent performance for both scanners. Our results suggest that the smartphone-based SLSS performance is sufficient to be used to create a complete three-dimensional model of the breast shape.
{"title":"iPhone TrueDepth cameras performance compared to optical 3D scanner for imaging the compressed breast shape","authors":"M. Pinto, J. Boita, K. Michielsen, I. Sechopoulos","doi":"10.1117/12.2622633","DOIUrl":"https://doi.org/10.1117/12.2622633","url":null,"abstract":"Modelling of the breast surface shape under compression in the cranio-caudal and medio-lateral oblique views could advance the development of image processing techniques and of dosimetric estimates in digital mammography and digital breast tomosynthesis. Our goal is to compare the performance of a previously tested and used optical structured light scanning system (SLSS) capable of capturing the breast shape under compression to that of an infrared smartphone-based SLSS. Their performance was compared by scanning a cuboid phantom and two breast shaped phantoms (30 mm and 74 mm thick). Ten scans of the cuboid phantom were acquired with each scanner, and the measured length and thickness of the scanned shape were compared against the ground truth and between the two scanners. The performance of the scanners regarding breast-like phantoms was evaluated by calculating the maximum and mean distance, along with the root mean square difference, between each scanners result and against the matching ground truth. The cuboid phantom analysis showed a statistical difference for the thickness measurement in both scanners and in the length measurement for the optical scanner (p<0.05). However, no statistical difference was found between the scanner measurements. For the breast-like phantoms, the higher maximum distances were found in the infrared scans, but the mean distance between ground truth surface and the scans showed equivalent performance for both scanners. Our results suggest that the smartphone-based SLSS performance is sufficient to be used to create a complete three-dimensional model of the breast shape.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"16 1","pages":"122860G - 122860G-5"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80693780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In breast cancer detection, change in findings throughout time is one of the major biomarkers for the presence of malignancy. Several studies have established the value of comparing mammograms with the ones from previous examinations. Some of them have shown that such comparison decreases the recall rate and increases the biopsy yield of cancer but does not increase the cancer detection rate. This evidence brought us to do the hypotheses that, as for human radiologists, adding temporal context information could be beneficial also for artificial intelligence (AI) systems for breast cancer detection thus improving their specificity which today represents the major limitation for an autonomous use of such AI systems. In this study we carry out a comparison between an AI system for breast cancer detection and an update version of the same system able to integrate the temporal context information.
{"title":"Including temporal changes information to an AI system for breast cancer detection to reduce false positive rate","authors":"S. Pacilé, C. Aguilar, S. Chambon, P. Fillard","doi":"10.1117/12.2624098","DOIUrl":"https://doi.org/10.1117/12.2624098","url":null,"abstract":"In breast cancer detection, change in findings throughout time is one of the major biomarkers for the presence of malignancy. Several studies have established the value of comparing mammograms with the ones from previous examinations. Some of them have shown that such comparison decreases the recall rate and increases the biopsy yield of cancer but does not increase the cancer detection rate. This evidence brought us to do the hypotheses that, as for human radiologists, adding temporal context information could be beneficial also for artificial intelligence (AI) systems for breast cancer detection thus improving their specificity which today represents the major limitation for an autonomous use of such AI systems. In this study we carry out a comparison between an AI system for breast cancer detection and an update version of the same system able to integrate the temporal context information.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"80 1","pages":"122860O - 122860O-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81586798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Sarno, G. Mettivier, K. Michielsen, J. J. Pautasso, I. Sechopoulos, P. Russo
This work proposes an empirical model for tuning spatial resolution and noise in simulated images in virtual clinical trials in x-ray breast imaging. In extending previous studies performed for direct conversion a-Se detectors used in digital mammography and digital breast tomosynthesis, this work introduces the model for the case of cone-beam computed tomography dedicated to the breast that uses a indirect conversion flat-panel detector. In the simulations, the detector is modeled as an absorbing layer whose material and thickness reflect those of the scintillator of the detector of a clinical scanner. The simulated images are then computed as a dose deposit map. The detector response curve, modulation transfer function (MTF) and noise power spectrum (NPS) were measured on a real detector. The same measurements were replicated in-silico for the simulated detector and scanner. The comparison of simulated and measured detector response curves permits to recover pixel values at the clinical scale. The difference between the simulated and measured MTFs permitted to introduce a linear filter for compensating simulated model simplification that determines a better spatial resolution in the simulated images with respect to real images. This filter presented a Gaussian shape in the Fourier domain with a standard deviation of 1.09 mm-1 , derived from those of the measured and simulated MTF curves, of 0.86 mm-1 and 1.41 mm-1 , respectively. Finally, the analysis of the NPS permits to compensate for noise characteristics due to the simulated model simplifications. The model applied to the simulated projection images produced MTF and normalized NPS in simulated 3D images, comparable to those obtained for the clinical scanner.
{"title":"Empirical detector model for simulated breast exams with a dedicated breast CT scanner","authors":"A. Sarno, G. Mettivier, K. Michielsen, J. J. Pautasso, I. Sechopoulos, P. Russo","doi":"10.1117/12.2624249","DOIUrl":"https://doi.org/10.1117/12.2624249","url":null,"abstract":"This work proposes an empirical model for tuning spatial resolution and noise in simulated images in virtual clinical trials in x-ray breast imaging. In extending previous studies performed for direct conversion a-Se detectors used in digital mammography and digital breast tomosynthesis, this work introduces the model for the case of cone-beam computed tomography dedicated to the breast that uses a indirect conversion flat-panel detector. In the simulations, the detector is modeled as an absorbing layer whose material and thickness reflect those of the scintillator of the detector of a clinical scanner. The simulated images are then computed as a dose deposit map. The detector response curve, modulation transfer function (MTF) and noise power spectrum (NPS) were measured on a real detector. The same measurements were replicated in-silico for the simulated detector and scanner. The comparison of simulated and measured detector response curves permits to recover pixel values at the clinical scale. The difference between the simulated and measured MTFs permitted to introduce a linear filter for compensating simulated model simplification that determines a better spatial resolution in the simulated images with respect to real images. This filter presented a Gaussian shape in the Fourier domain with a standard deviation of 1.09 mm-1 , derived from those of the measured and simulated MTF curves, of 0.86 mm-1 and 1.41 mm-1 , respectively. Finally, the analysis of the NPS permits to compensate for noise characteristics due to the simulated model simplifications. The model applied to the simulated projection images produced MTF and normalized NPS in simulated 3D images, comparable to those obtained for the clinical scanner.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"49 1","pages":"1228605 - 1228605-6"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90857651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)
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