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
Kazuya Abe, Soma Kudo, Hideya Takeo, Yuichi Nagai, S. Nawano
In recent years, the amount of images to be read has increased due to the higher resolution of diagnostic imaging devices, and the burden on doctors has also increased. To solve this problem, the improvement of CAD (computer-aided diagnosis) performance has been studied. In this study, we developed an AI-based system for discriminating benign and malignant breast cancer tumors using transfer learning, one of the deep learning methods of AI, and analyzed what factors are necessary to improve the diagnostic accuracy of the system. Classification of benign and malignant diseases using diagnostic images showed an accuracy of 90%, which was equivalent to physician's discrimination, but the accuracy for medical checkup images was low at 85%, and image comparison revealed that this was due to noise and low contrast. We analyzed that these improvements are necessary for the construction of a more accurate CAD system for medical checkup images.
{"title":"Analysis of the effects of image quality differences on CAD performance in AI-based benign-malignant discrimination processing of breast masses","authors":"Kazuya Abe, Soma Kudo, Hideya Takeo, Yuichi Nagai, S. Nawano","doi":"10.1117/12.2623398","DOIUrl":"https://doi.org/10.1117/12.2623398","url":null,"abstract":"In recent years, the amount of images to be read has increased due to the higher resolution of diagnostic imaging devices, and the burden on doctors has also increased. To solve this problem, the improvement of CAD (computer-aided diagnosis) performance has been studied. In this study, we developed an AI-based system for discriminating benign and malignant breast cancer tumors using transfer learning, one of the deep learning methods of AI, and analyzed what factors are necessary to improve the diagnostic accuracy of the system. Classification of benign and malignant diseases using diagnostic images showed an accuracy of 90%, which was equivalent to physician's discrimination, but the accuracy for medical checkup images was low at 85%, and image comparison revealed that this was due to noise and low contrast. We analyzed that these improvements are necessary for the construction of a more accurate CAD system for medical checkup 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":"24 1","pages":"122860R - 122860R-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81705589","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}
M. Hill, Linda Martis, M. Halling-Brown, R. Highnam, A. Chan
Purpose: To identify mammographic image quality indicators (IQI) predictive of interval breast cancers (IC) as opposed to screen-detected cancers (SDC). Methods: Eligible cases for the study were raw, routine recall, screening exams acquired at two UK sites between 2010- 2018, from the OPTIMAM database. Women were matched 3:1 (SDC, n=965 versus IC, n=326), by age (nearest), screening site, breast density grade, Xray system vendor, and compression paddle. Images of the affected breast for prior (IC only) or incident (SDC only) exams were processed using automated software to obtain volumetric breast density (VBD) and IQI metrics related to compression and breast positioning. Compression pressure (CP) was categorised into tertiles or low/target/high (<7/7-15/<15 kPa) groups. Univariate and logistic regression analyses were used to identify significant predictors of IC versus SDC. Results: Compared to SDC, IC had lower median CP (7.9 versus 8.6 kPa, p<0.05). Multivariate analysis found only CP to be significantly associated with the risk of IC versus SDC, with odds ratios (OR) and 95% confidence intervals of 0.93 (0.89-0.97) per unit CP. Compared to low CP, target CP was significantly associated with a lower IC versus SDC risk at the breast level [OR=0.73 (0.56-0.95)] and for mediolateral oblique views [OR=0.77 (0.59-0.99)]. Comparing the third and first tertile, CP was significantly associated with lower risk of IC versus SDC [0.64 (0.47-0.87)], with very similar results when analysed per view. Conclusions: CP was found to be a significant predictor of IC versus SDC, with higher CP being associated with a lower risk of IC.
目的:确定乳房x线图像质量指标(IQI)预测间隔期乳腺癌(IC)，而不是筛查发现的癌症(SDC)。方法:符合研究条件的病例是2010年至2018年期间在英国两个地点从OPTIMAM数据库中获得的原始、常规召回和筛查检查。女性按年龄(最近)、筛查地点、乳腺密度等级、x射线系统供应商和压缩桨进行3:1匹配(SDC, n=965 vs IC, n=326)。使用自动化软件处理先前(仅限IC)或事件(仅限SDC)检查的受影响乳房图像，以获得与压缩和乳房定位相关的乳腺体积密度(VBD)和IQI指标。压缩压力(CP)分为低/目标/高(<7/7-15/<15 kPa)组。采用单变量和逻辑回归分析来确定IC与SDC的显著预测因素。结果:与SDC相比，IC的中位CP较低(7.9比8.6 kPa, p<0.05)。多因素分析发现，只有CP与IC和SDC的风险显著相关，比值比(OR)和95%置信区间为0.93(0.89-0.97)/单位CP。与低CP相比，靶CP在乳房水平与较低的IC和SDC风险显著相关[OR=0.73(0.56-0.95)]和中侧斜位[OR=0.77(0.59-0.99)]。比较第三和第一分位数，CP与较低的IC与SDC风险显著相关[0.64(0.47-0.87)]，当每个视图分析时结果非常相似。结论:CP被发现是IC与SDC的重要预测因子，较高的CP与较低的IC风险相关。
{"title":"Mammographic compression pressure as a predictor of interval cancer","authors":"M. Hill, Linda Martis, M. Halling-Brown, R. Highnam, A. Chan","doi":"10.1117/12.2625460","DOIUrl":"https://doi.org/10.1117/12.2625460","url":null,"abstract":"Purpose: To identify mammographic image quality indicators (IQI) predictive of interval breast cancers (IC) as opposed to screen-detected cancers (SDC). Methods: Eligible cases for the study were raw, routine recall, screening exams acquired at two UK sites between 2010- 2018, from the OPTIMAM database. Women were matched 3:1 (SDC, n=965 versus IC, n=326), by age (nearest), screening site, breast density grade, Xray system vendor, and compression paddle. Images of the affected breast for prior (IC only) or incident (SDC only) exams were processed using automated software to obtain volumetric breast density (VBD) and IQI metrics related to compression and breast positioning. Compression pressure (CP) was categorised into tertiles or low/target/high (<7/7-15/<15 kPa) groups. Univariate and logistic regression analyses were used to identify significant predictors of IC versus SDC. Results: Compared to SDC, IC had lower median CP (7.9 versus 8.6 kPa, p<0.05). Multivariate analysis found only CP to be significantly associated with the risk of IC versus SDC, with odds ratios (OR) and 95% confidence intervals of 0.93 (0.89-0.97) per unit CP. Compared to low CP, target CP was significantly associated with a lower IC versus SDC risk at the breast level [OR=0.73 (0.56-0.95)] and for mediolateral oblique views [OR=0.77 (0.59-0.99)]. Comparing the third and first tertile, CP was significantly associated with lower risk of IC versus SDC [0.64 (0.47-0.87)], with very similar results when analysed per view. Conclusions: CP was found to be a significant predictor of IC versus SDC, with higher CP being associated with a lower risk of IC.","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":"61 1","pages":"1228612 - 1228612-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90268502","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 cancer is one of the deadliest diseases. It is affecting majority of women world wide. Computer Aided Diagnosis (CAD) systems can be used to help radiologists in order to examine the initial symptoms. One of the early symptoms is micro-calcifications. Detection of abnormalities is an essential part of treatment in the right direction. Along with detection of abnormalities, the classification of micro-calcification has a vital importance. Timely detection and classification of micro-calcification as malignant or benign can save a lot of women. We have used region based convolutional neural networks and obtained 92.7% mean average precision at training time while at testing time mAP is 89.2%.
{"title":"Automatic classification and detection of abnormalities in mammograms using deep learning","authors":"Adeela Islam, Zobia Suhail","doi":"10.1117/12.2624216","DOIUrl":"https://doi.org/10.1117/12.2624216","url":null,"abstract":"Breast cancer is one of the deadliest diseases. It is affecting majority of women world wide. Computer Aided Diagnosis (CAD) systems can be used to help radiologists in order to examine the initial symptoms. One of the early symptoms is micro-calcifications. Detection of abnormalities is an essential part of treatment in the right direction. Along with detection of abnormalities, the classification of micro-calcification has a vital importance. Timely detection and classification of micro-calcification as malignant or benign can save a lot of women. We have used region based convolutional neural networks and obtained 92.7% mean average precision at training time while at testing time mAP is 89.2%.","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":"441 1","pages":"122860V - 122860V-10"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76963645","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}
Marthe Picard, L. Cockmartin, Kristin Buelens, S. Postema, V. Celis, Cédric Aesseloos, H. Bosmans
Early detection of breast cancer through mammographic screening can only be achieved with high quality mammograms. In this study an experienced radiologist and radiographer scored 127 mammographic screening exams with MLO and CC views of left and right breasts using 18 different positioning quality criteria. This subjective evaluation of the positioning quality was compared to the objective and automatic assessment by Volpara TruPGMI (Volpara Health, New Zealand). The quality criteria on missed tissue at medial or lateral side of the breast were in agreement with the software for the radiographer but was scored differently by the radiologist. The criterion on the nipple in profile showed good agreement between the readers and the software. The important criterion on the number of images that had to be repeated showed that even though the same amount of cases was rated to be repeated, the majority of the cases were discordant between radiologist and software, the agreement with the radiographer was better. The presence of folds in the pectoral muscle, the adequate depiction of the pectoral muscle and inframammary angle on MLO view showed an acceptable agreement between the readers and software. Finally, the overall positioning quality was rated as Perfect, Good, Moderate or Inadequate. The extreme ratings of Perfect and Inadequate showed high agreement between readers and software. However the number of intermediate ratings “Moderate” and “Good” were very different. For the readers the majority of the images was “Good” whereas the software scored most often “Moderate”. Subjective positioning quality monitoring is prone to high reader variability; this can be overcome via the use of automatic measurements with software. Nevertheless, prior to the use of automatic quality monitoring software in clinical practice, a careful evaluation and validation is needed.
{"title":"Objective and subjective assessment of mammographic positioning quality","authors":"Marthe Picard, L. Cockmartin, Kristin Buelens, S. Postema, V. Celis, Cédric Aesseloos, H. Bosmans","doi":"10.1117/12.2624400","DOIUrl":"https://doi.org/10.1117/12.2624400","url":null,"abstract":"Early detection of breast cancer through mammographic screening can only be achieved with high quality mammograms. In this study an experienced radiologist and radiographer scored 127 mammographic screening exams with MLO and CC views of left and right breasts using 18 different positioning quality criteria. This subjective evaluation of the positioning quality was compared to the objective and automatic assessment by Volpara TruPGMI (Volpara Health, New Zealand). The quality criteria on missed tissue at medial or lateral side of the breast were in agreement with the software for the radiographer but was scored differently by the radiologist. The criterion on the nipple in profile showed good agreement between the readers and the software. The important criterion on the number of images that had to be repeated showed that even though the same amount of cases was rated to be repeated, the majority of the cases were discordant between radiologist and software, the agreement with the radiographer was better. The presence of folds in the pectoral muscle, the adequate depiction of the pectoral muscle and inframammary angle on MLO view showed an acceptable agreement between the readers and software. Finally, the overall positioning quality was rated as Perfect, Good, Moderate or Inadequate. The extreme ratings of Perfect and Inadequate showed high agreement between readers and software. However the number of intermediate ratings “Moderate” and “Good” were very different. For the readers the majority of the images was “Good” whereas the software scored most often “Moderate”. Subjective positioning quality monitoring is prone to high reader variability; this can be overcome via the use of automatic measurements with software. Nevertheless, prior to the use of automatic quality monitoring software in clinical practice, a careful evaluation and validation is needed.","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":"238 1","pages":"122860F - 122860F-10"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77640173","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}
Arthur C. Costa, R. B. Vimieiro, L. Borges, B. Barufaldi, Andrew D. A. Maidment, M. Vieira
The angular range and number of projections are parameters that directly influence the image quality and the visibility of lesions in digital breast tomosynthesis (DBT). The medical field is taking advantage of the increasing performance of machine learning algorithms with the use of complex data-driven models, known as deep learning (DL) networks. The use of DL has also been highlighted in the tasks of video frame interpolation (VFI) for the synthesis of new images in order to increase the frame rate per second. In the present work, we use a residual refinement interpolation network (RRIN) to generate new synthetic DBT projections from pairs of real projections. We studied two different approaches: first, we increased the number of projections before reconstruction using the synthetic images, with the aim of improving the quality of the reconstructed slices without increasing the radiation dose to the patient. In the second, we investigated the effect of replacing existing projections with synthetic ones, with the objective of reducing the radiation dose and acquisition time. In the first approach, we used virtual phantoms to generate sets of DBT projections to train the network. We then evaluated the contrast-to-noise ratio (CNR) of simulated microcalcifications after reconstruction. The CNR was higher for all sets where supplementary images were added compared to those with only real images. In the second approach, we trained the network with clinical data and tested it with images acquired with a physical anthropomorphic breast phantom. Both the projections and the slices showed good similarity with the real ones, suggesting that the use of VFI networks to generate DBT projections is promising. However, further studies should be carried out to assess the feasibility of this approach.
{"title":"Assessment of video frame interpolation network to generate digital breast tomosynthesis projections","authors":"Arthur C. Costa, R. B. Vimieiro, L. Borges, B. Barufaldi, Andrew D. A. Maidment, M. Vieira","doi":"10.1117/12.2625748","DOIUrl":"https://doi.org/10.1117/12.2625748","url":null,"abstract":"The angular range and number of projections are parameters that directly influence the image quality and the visibility of lesions in digital breast tomosynthesis (DBT). The medical field is taking advantage of the increasing performance of machine learning algorithms with the use of complex data-driven models, known as deep learning (DL) networks. The use of DL has also been highlighted in the tasks of video frame interpolation (VFI) for the synthesis of new images in order to increase the frame rate per second. In the present work, we use a residual refinement interpolation network (RRIN) to generate new synthetic DBT projections from pairs of real projections. We studied two different approaches: first, we increased the number of projections before reconstruction using the synthetic images, with the aim of improving the quality of the reconstructed slices without increasing the radiation dose to the patient. In the second, we investigated the effect of replacing existing projections with synthetic ones, with the objective of reducing the radiation dose and acquisition time. In the first approach, we used virtual phantoms to generate sets of DBT projections to train the network. We then evaluated the contrast-to-noise ratio (CNR) of simulated microcalcifications after reconstruction. The CNR was higher for all sets where supplementary images were added compared to those with only real images. In the second approach, we trained the network with clinical data and tested it with images acquired with a physical anthropomorphic breast phantom. Both the projections and the slices showed good similarity with the real ones, suggesting that the use of VFI networks to generate DBT projections is promising. However, further studies should be carried out to assess the feasibility of this approach.","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":"377 1","pages":"122861D - 122861D-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79041255","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}
G. Mettivier, Roberta Ricciarci, A. Sarno, F. S. Maddaloni, M. Porzio, M. Staffa, Salvatori Minelli, A. Santoro, E. Antignani, M. Masi, V. Landoni, P. Ordoñez, F. Ferranti, Laura Greco, S. Clemente, P. Russo
The aim of the DeepLook project, funded by INFN (Italy), is to implement a deep learning architecture for Computed Aided Detection (CAD), based on neural networks developed with deep learning methods, for the automatic detection and classification of breast lesions in DBT images. A preliminary step (started 2 years ago and still ongoing) was the creation of a dataset of annotated images. This dataset includes images acquired with different clinical DBT units and different acquisition geometries, on several hundred patients, containing a variety of possible breast lesions and normal cases of absence of lesions. This will make the diagnostic capacity of the CAD system particularly extensive in various clinical situations and on a significant sample of patients, so allowing the network to diagnose various types of lesions (at the level of the single tomosynthesis slices) and capable of operate on commercial DBT systems, also available from different vendors, as found in breast diagnosis departments. The developed CAD and first result of the indication of the slice containing the suspected mass will be presented.
{"title":"DeepLook: a deep learning computed diagnosis support for breast tomosynthesis","authors":"G. Mettivier, Roberta Ricciarci, A. Sarno, F. S. Maddaloni, M. Porzio, M. Staffa, Salvatori Minelli, A. Santoro, E. Antignani, M. Masi, V. Landoni, P. Ordoñez, F. Ferranti, Laura Greco, S. Clemente, P. Russo","doi":"10.1117/12.2625369","DOIUrl":"https://doi.org/10.1117/12.2625369","url":null,"abstract":"The aim of the DeepLook project, funded by INFN (Italy), is to implement a deep learning architecture for Computed Aided Detection (CAD), based on neural networks developed with deep learning methods, for the automatic detection and classification of breast lesions in DBT images. A preliminary step (started 2 years ago and still ongoing) was the creation of a dataset of annotated images. This dataset includes images acquired with different clinical DBT units and different acquisition geometries, on several hundred patients, containing a variety of possible breast lesions and normal cases of absence of lesions. This will make the diagnostic capacity of the CAD system particularly extensive in various clinical situations and on a significant sample of patients, so allowing the network to diagnose various types of lesions (at the level of the single tomosynthesis slices) and capable of operate on commercial DBT systems, also available from different vendors, as found in breast diagnosis departments. The developed CAD and first result of the indication of the slice containing the suspected mass will be presented.","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":"17 1","pages":"122860P - 122860P-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80229979","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}
V. Ravaglia, S. Farnedi, G. Guerra, N. Scrittori, G. Venturi
The aim of the study is to assess the feasibility of a homemade phantom for image quality evaluation in Contrast Enhanced Spectral Mammography (CESM). The phantom was composed by a PMMA slab with holes of different diameters (10, 5 and 2.5 mm) and thicknesses (5, 4, 3 and 2 mm) filled with diluted iodine contrast medium, resulting in concentrations of 1.9, 1.5, 1.1 and 0.7 mg/cm2 (±0.2 mg/cm2 ), similar to the clinical concentrations. Furthermore, we added tissue-equivalent slabs with anatomical background and we simulated 3 different configurations equivalent to 32, 60 and 90 mm breast thicknesses. Image acquisitions were performed on a Hologic 3Dimensions mammography system using AEC clinical parameters. The acquisitions included a low energy exposure followed by an high energy one, and the resulting processed images were a subtraction of the 2 acquired images. For each configuration, the CNR on the low, high and subtracted images were calculated. The results showed that CNR values measured on the processed subtracted images were much higher respect to the CNR measured on the “for processing” low and high energy images. Furthermore, as expected, an increase in CNR for increasing iodine concentration was verified on the processed images, but not always on raw images that contained anatomical background. Preliminary results showed that the phantom is suitable for image quality evaluation in CESM but further studies with different acquisition parameters and on different mammography systems are necessary to assess the repeatability and the consistency of the measurements.
{"title":"A homemade phantom for image quality evaluation in contrast enhanced spectral mammography (CESM)","authors":"V. Ravaglia, S. Farnedi, G. Guerra, N. Scrittori, G. Venturi","doi":"10.1117/12.2622206","DOIUrl":"https://doi.org/10.1117/12.2622206","url":null,"abstract":"The aim of the study is to assess the feasibility of a homemade phantom for image quality evaluation in Contrast Enhanced Spectral Mammography (CESM). The phantom was composed by a PMMA slab with holes of different diameters (10, 5 and 2.5 mm) and thicknesses (5, 4, 3 and 2 mm) filled with diluted iodine contrast medium, resulting in concentrations of 1.9, 1.5, 1.1 and 0.7 mg/cm2 (±0.2 mg/cm2 ), similar to the clinical concentrations. Furthermore, we added tissue-equivalent slabs with anatomical background and we simulated 3 different configurations equivalent to 32, 60 and 90 mm breast thicknesses. Image acquisitions were performed on a Hologic 3Dimensions mammography system using AEC clinical parameters. The acquisitions included a low energy exposure followed by an high energy one, and the resulting processed images were a subtraction of the 2 acquired images. For each configuration, the CNR on the low, high and subtracted images were calculated. The results showed that CNR values measured on the processed subtracted images were much higher respect to the CNR measured on the “for processing” low and high energy images. Furthermore, as expected, an increase in CNR for increasing iodine concentration was verified on the processed images, but not always on raw images that contained anatomical background. Preliminary results showed that the phantom is suitable for image quality evaluation in CESM but further studies with different acquisition parameters and on different mammography systems are necessary to assess the repeatability and the consistency of the measurements.","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":"17 1","pages":"122860I - 122860I-6"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75104635","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}
S. Said, M. Meyling, R. Huguenot, M. Horning, P. Clauser, N. Ruiter, P. Baltzer, T. Hopp
In multimodal diagnosis for early breast cancer detection, spatial alignment by means of image registration is an important task. We develop patient-specific biomechanical models of the breast, for which one of the challenges is automatic segmentation for magnetic resonance imaging (MRI) of the breast. In this paper, we propose a novel method using unsupervised neural networks with pre-processing and post-processing to enable automatic breast MRI segmentation for three tissue types simultaneously: fatty, glandular, and muscular tissue. Pre-processing aims at facilitating training of the network. The architecture of neural network is a Kanezaki-net extended to 3D and consists of two sub-networks. Post-processing is enhancing the obtained segmentations by removing common errors. 25 datasets of T2 weighted MRI from the Medical University of Vienna have been evaluated qualitatively by two observers while eight datasets have been evaluated quantitatively based on a ground truth annotated by a medical practitioner. As a result of the qualitative evaluation, 22 out of 25 are usable for biomechanical models. Quantitatively, we achieved an average dice coefficient of 0.88 for fatty tissue, 0.5 for glandular tissue, and 0.86 for muscular tissue. The proposed method can serve as a robust method for automatic generation of biomechanical models.
{"title":"MRI breast segmentation using unsupervised neural networks for biomechanical models","authors":"S. Said, M. Meyling, R. Huguenot, M. Horning, P. Clauser, N. Ruiter, P. Baltzer, T. Hopp","doi":"10.1117/12.2624245","DOIUrl":"https://doi.org/10.1117/12.2624245","url":null,"abstract":"In multimodal diagnosis for early breast cancer detection, spatial alignment by means of image registration is an important task. We develop patient-specific biomechanical models of the breast, for which one of the challenges is automatic segmentation for magnetic resonance imaging (MRI) of the breast. In this paper, we propose a novel method using unsupervised neural networks with pre-processing and post-processing to enable automatic breast MRI segmentation for three tissue types simultaneously: fatty, glandular, and muscular tissue. Pre-processing aims at facilitating training of the network. The architecture of neural network is a Kanezaki-net extended to 3D and consists of two sub-networks. Post-processing is enhancing the obtained segmentations by removing common errors. 25 datasets of T2 weighted MRI from the Medical University of Vienna have been evaluated qualitatively by two observers while eight datasets have been evaluated quantitatively based on a ground truth annotated by a medical practitioner. As a result of the qualitative evaluation, 22 out of 25 are usable for biomechanical models. Quantitatively, we achieved an average dice coefficient of 0.88 for fatty tissue, 0.5 for glandular tissue, and 0.86 for muscular tissue. The proposed method can serve as a robust method for automatic generation of biomechanical models.","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":"82 1","pages":"122860C - 122860C-7"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76366611","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}
L. Borges, M. Brochi, M. Vieira, P. M. de Azevedo-Marques
Image quality directly influences the accuracy of lesion detection and characterization in x-ray mammograms. Thus, it is crucial that acceptable image quality is maintained while using as little ionizing radiation as possible. In this scenario, denoising plays an important role in recovering image quality while keeping constant radiation dose. Although most ‘off-the-shelf’ denoising algorithms assume signal-independent and frequency-independent (white) Gaussian noise, in x-ray generation and detection this assumption is seldom valid. In this work we leverage a recently published variance-stabilizing transform and a frequency-dependent denoising algorithm to address signal-dependent and frequency-dependent denoising of x-ray mammograms subject to structural and correlated noise. To illustrate the application of the proposed pipeline, we restored synthetic mammograms generated by a virtual clinical trial platform. The results showed that the denoising pipeline was able to recover the quality of mammograms acquired at lower radiation levels to achieve similar image quality of full-dose acquisitions, in terms of the QILV, residual variance and power spectrum metrics. The bias2 metric indicates that even though the pipeline is able to achieve very similar noise levels to a full-dose acquisition, there is a penalty to the signal, which becomes biased due to blur and smearing as the dose level is reduced.
{"title":"Denoising of mammograms subject to structural and spatially-correlated noise: a virtual clinical trial","authors":"L. Borges, M. Brochi, M. Vieira, P. M. de Azevedo-Marques","doi":"10.1117/12.2626629","DOIUrl":"https://doi.org/10.1117/12.2626629","url":null,"abstract":"Image quality directly influences the accuracy of lesion detection and characterization in x-ray mammograms. Thus, it is crucial that acceptable image quality is maintained while using as little ionizing radiation as possible. In this scenario, denoising plays an important role in recovering image quality while keeping constant radiation dose. Although most ‘off-the-shelf’ denoising algorithms assume signal-independent and frequency-independent (white) Gaussian noise, in x-ray generation and detection this assumption is seldom valid. In this work we leverage a recently published variance-stabilizing transform and a frequency-dependent denoising algorithm to address signal-dependent and frequency-dependent denoising of x-ray mammograms subject to structural and correlated noise. To illustrate the application of the proposed pipeline, we restored synthetic mammograms generated by a virtual clinical trial platform. The results showed that the denoising pipeline was able to recover the quality of mammograms acquired at lower radiation levels to achieve similar image quality of full-dose acquisitions, in terms of the QILV, residual variance and power spectrum metrics. The bias2 metric indicates that even though the pipeline is able to achieve very similar noise levels to a full-dose acquisition, there is a penalty to the signal, which becomes biased due to blur and smearing as the dose level is reduced.","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":"10 1","pages":"1228606 - 1228606-6"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75987154","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}
T. Wagner, L. Cockmartin, N. Marshall, Y. Wang, H. Bosmans
Aim: To assess the difference in radiomic feature values between pairs of mammographic images used for processing(FOR PROC) and for presentation(FOR PRES) as well as the ability to determine the BIRADS density classification from these radiomic features with different classification models. Methods: A dataset of FOR PROC and FOR PRES image pairs annotated with labels for the BI-RADS classification done by a radiologist is used in this study. The differences in radiomic feature values between the image types are evaluated with the intraclass correlation coefficient(ICC). Additionally, the discriminative power of radiomic feature values regarding the BI-RADS score is evaluated with Logistic Regression, Random Forest and a 5-layer deep Neural Network. The results of these models are evaluated with a 5-fold crossvalidation. Results: The reliability of radiomic feature is generally low between pairs of FOR PROC and FOR PRES images for all radiomic feature groups. Furthermore, the simple models used to determine the ability to assign the BI-RADS density classification based on the radiomic feature values reached insufficient accuracy to be considered adequate. Conclusion: The study revealed low reliability between both image types. Furthermore radiomic features alone seem to be insufficient to determine the BI-RADS classification using simple models.
{"title":"Comprehensive study on the difference in radiomic feature values between for-processing and for-presentation mammographic images and their discriminative power regarding BI-RADS density classification","authors":"T. Wagner, L. Cockmartin, N. Marshall, Y. Wang, H. Bosmans","doi":"10.1117/12.2625776","DOIUrl":"https://doi.org/10.1117/12.2625776","url":null,"abstract":"Aim: To assess the difference in radiomic feature values between pairs of mammographic images used for processing(FOR PROC) and for presentation(FOR PRES) as well as the ability to determine the BIRADS density classification from these radiomic features with different classification models. Methods: A dataset of FOR PROC and FOR PRES image pairs annotated with labels for the BI-RADS classification done by a radiologist is used in this study. The differences in radiomic feature values between the image types are evaluated with the intraclass correlation coefficient(ICC). Additionally, the discriminative power of radiomic feature values regarding the BI-RADS score is evaluated with Logistic Regression, Random Forest and a 5-layer deep Neural Network. The results of these models are evaluated with a 5-fold crossvalidation. Results: The reliability of radiomic feature is generally low between pairs of FOR PROC and FOR PRES images for all radiomic feature groups. Furthermore, the simple models used to determine the ability to assign the BI-RADS density classification based on the radiomic feature values reached insufficient accuracy to be considered adequate. Conclusion: The study revealed low reliability between both image types. Furthermore radiomic features alone seem to be insufficient to determine the BI-RADS classification using simple models.","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":"10 1","pages":"1228615 - 1228615-11"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80599330","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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