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

{"title":"Synthetic data of simulated microcalcification clusters to train and explain deep learning detection models in contrast-enhanced mammography","authors":"A. Van Camp, M. Beuque, L. Cockmartin, H. Woodruff, N. Marshall, M. Lobbes, P. Lambin, H. Bosmans","doi":"10.1117/12.2621195","DOIUrl":"https://doi.org/10.1117/12.2621195","url":null,"abstract":"Deep learning (DL) models can be trained on contrast-enhanced mammography (CEM) images to detect and classify lesions in the breast. As they often put more emphasis on the masses enhanced in the recombined image, they can fail in recognizing microcalcification clusters since these are hardly enhanced and are mainly visible in the (processed) lowenergy image. Therefore, we developed a method to create synthetic data with simulated microcalcification clusters to be used for data augmentation and explainability studies when training DL models. At first 3-dimensional voxel models of simulated microcalcification clusters based on descriptors of the shape and structure were constructed. In a set of 500 simulated microcalcification clusters the range of the size and of the number of microcalcifications per cluster followed the distribution of real clusters. The insertion of these clusters in real images of non-delineated CEM cases was evaluated by radiologists. The realism score was acceptable for single view applications. Radiologists could more easily categorize synthetic clusters into benign versus malignant than real clusters. In a second phase of the work, the role of synthetic data for training and/or explaining DL models was explored. A Mask R-CNN model was trained with synthetic CEM images containing microcalcification clusters. After a training run of 100 epochs the model was found to overfit on a training set of 192 images. In an evaluation with multiple test sets, it was found that this high level of sensitivity was due to the model being capable of recognizing the image rather than the cluster. Synthetic data could be applied for more tests, such as the impact of particular features in both background and lesion 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":"85 1","pages":"122860U - 122860U-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76853379","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}

{"title":"Correspondence between areas causing recall in breast cancer screening and artificial intelligence findings","authors":"Victor Dahlblom, A. Tingberg, S. Zackrisson, M. Dustler","doi":"10.1117/12.2625731","DOIUrl":"https://doi.org/10.1117/12.2625731","url":null,"abstract":"False positive recall is a major issue in breast cancer screening and the introduction of artificial intelligence (AI) might affect which women who are unnecessarily recalled. We have investigated how an AI system works on false positive recalls at screening and compared with radiologist findings. Two-view digital mammography (DM) examinations from 656 recalled women (136 with screening detected cancer), were analysed with a commercial AI system. The AI findings were matched with the areas on the images causing the recalls. The agreement was studied both at the examination level and for individual findings. Scores were compared between true positive and false positive recalls. ROC analysis was used to study the AI-system’s ability to distinguish between true and false positive recalls. It was also studied how the AI system performed on cases where there were discordant readings. AI identified the same areas as radiologists in 80% of the cases recalled on DM. For true positives both the proportion of matching areas and AI scores were higher than for false positive recalls. The AI system also had a relatively large AUC (0.83) for differentiating between false positive recalls and cancers. Further, the AI system identified most of the findings leading to recall in cases where only one of the readers had marked the case for discussion. There is a relatively large agreement between the AI system and radiologists. The AI system scores the false positives lower than true positives. AI complements a single reader in a way similar to a second reader.","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":"7 1","pages":"122860K - 122860K-7"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74000425","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}

{"title":"Using deep learning for triple-negative breast cancer classification in DCE-MRI","authors":"Joel Vidal, R. Martí","doi":"10.1117/12.2625780","DOIUrl":"https://doi.org/10.1117/12.2625780","url":null,"abstract":"Triple-negative is one of the most aggressive type of breast cancer for which is also difficult to find an effective treatment. An early diagnosis and a fast and specific treatment are shown to be key aspects for a better prognosis. Current diagnosis of these cases are based on performing a biopsy. This study proposes a non-invasive medical imaging predication method, based on a deep learning architecture, to automatically classify triple-negative tumors in DCE-MRI images. Results are evaluated on an extensive public dataset for different normalizations, data augmentations, learning rates and batch sizes, reaching a state-of-the-art AUC of 0.68.","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":"118 1","pages":"122860X - 122860X-6"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77927196","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}

{"title":"Simultaneous pectoral muscle and nipple location in MLO mammograms, considering image quality assumptions","authors":"E. García, R. Martí, J. Martí, J. del Riego, Cecilia Aynes, A. Oliver, Oliver Díaz","doi":"10.1117/12.2625778","DOIUrl":"https://doi.org/10.1117/12.2625778","url":null,"abstract":"Feature-based registration algorithms can be used to establish spatial correspondence between two image. Therefore, anatomical landmarks such as the breast boundary, pectoral muscle, nipple, duct and vessels need to be considered. The aim of this paper is to introduce a new approach which combine the pectoral muscle segmentation and nipple location, considering mammography quality assumptions. Pectoral muscle is initialized as a straight line from the top of the image to the nipple level. Afterwards, both pectoral muscle boundary and nipple position are optimized using an iterative approach. The results show that the nipple is localized on the contour of the corresponding area (error smaller than 10 mm) while the Dice’s coefficient of the pectoral muscle segmentation is equal to 0.84 ± 0.12 using a straight line which is improved using a Chan-Vese active contour approach, reaching 0.87 ± 0.13. Our algorithm is easily generalized and portable to a different mammographic system since it barely depends on images statistics -i.e. pixel intensity values-, and is just based on geometrical considerations.","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":"13 1","pages":"122860E - 122860E-7"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89081581","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}

{"title":"Using virtual clinical trials to assess objective image quality metrics in the task of microcalcification localization in digital mammography","authors":"L. E. Soares, L. Borges, B. Barufaldi, Andrew D. A. Maidment, M. Vieira","doi":"10.1117/12.2625745","DOIUrl":"https://doi.org/10.1117/12.2625745","url":null,"abstract":"Many works have investigated methods to assess the quality of mammography images using objective image quality metrics. However, few studies have evaluated the ability of these metrics to predict the performance of human observers on specific tasks related to mammographic examination that are highly dependent on image quality. The propose of this work is to evaluate the quality of digital mammography acquired at a range of radiation doses through a set of objective metrics and to compare the results with the performance of human observers in the task of locating microcalcification clusters in these images. A dataset of 100 synthetic mammograms was simulated using a virtual clinical trials software. Microcalcification clusters of different sizes and contrasts were computationally inserted into the images. Acquisitions with five different radiation doses were simulated using a noise injection method proposed in a previous work. Four medical physicists with experience in analysis of mammographic images participated in the microcalcification cluster localization tests. The quality of digital mammography images was assessed considering nine well-known objective metrics. The metrics were calculated on both the raw data (DICOM ‘for processing’ tag) and the processed images (DICOM ‘for presentation’ tag). Finally, the association between readers performance and image quality index was conducted by calculating the percentage variation of all metrics as a function of radiation dose, taking the standard dose as a reference. Although the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are the most used in the literature, our results showed that Quality Index based on Local Variance (QILV) is the objective metric that best describes the behavior of human visual perception with the variation of radiation dose in digital mammography.","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":"1228603 - 1228603-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85352645","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}

{"title":"Sharing generative models instead of private data: a simulation study on mammography patch classification","authors":"Zuzanna Szafranowska, Richard Osuala, Bennet Breier, Kaisar Kushibar, K. Lekadir, Oliver Díaz","doi":"10.1117/12.2625781","DOIUrl":"https://doi.org/10.1117/12.2625781","url":null,"abstract":"Early detection of breast cancer in mammography screening via deep-learning based computer-aided detection systems shows promising potential in improving the curability and mortality rates of breast cancer. However, many clinical centres are restricted in the amount and heterogeneity of available data to train such models to (i) achieve promising performance and to (ii) generalise well across acquisition protocols and domains. As sharing data between centres is restricted due to patient privacy concerns, we propose a potential solution: sharing trained generative models between centres as substitute for real patient data. In this work, we use three well known mammography datasets to simulate three different centres, where one centre receives the trained generator of Generative Adversarial Networks (GANs) from the two remaining centres in order to augment the size and heterogeneity of its training dataset. We evaluate the utility of this approach on mammography patch classification on the test set of the GAN-receiving centre using two different classification models, (a) a convolutional neural network and (b) a transformer neural network. Our experiments demonstrate that shared GANs notably increase the performance of both transformer and convolutional classification models and highlight this approach as a viable alternative to inter-centre data sharing.","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":"70 1","pages":"122860Q - 122860Q-9"},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82351545","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}

{"title":"Front Matter: Volume 10718","authors":"Iwbi, E. Krupinski","doi":"10.1117/12.2502754","DOIUrl":"https://doi.org/10.1117/12.2502754","url":null,"abstract":"","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":"102 1","pages":"1071801"},"PeriodicalIF":0.0,"publicationDate":"2018-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77285873","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}

{"title":"The Impact of Reduced Injected Radioactivity on Image Quality of Molecular Breast Imaging Tomosynthesis.","authors":"Olivia Sullivan,&nbsp;Zongyi Gong,&nbsp;Kelly Klanian,&nbsp;Tushita Patel,&nbsp;Mark B Williams","doi":"10.1007/978-3-642-31271-7_39","DOIUrl":"https://doi.org/10.1007/978-3-642-31271-7_39","url":null,"abstract":"<p><p>This study's objective is to compare image quality in 3-D molecular breast imaging tomosynthesis (MBIT) with that in planar molecular breast imaging (MBI) over a range of breast radioactivity concentrations. Using gelatin and point source phantoms lesion contrast, lesion signal-to-noise ratio (SNR) and spatial resolution were compared for a range of lesion sizes and depths. For both MBI and MBIT, lesion contrast is essentially constant with changing activity while SNR decreases by a factor of 1.5 - 2 between 100% and 25% activity levels. For nearly all lesion sizes and locations contrast and SNR are significantly higher for MBIT than MBI, potentially permitting greater reductions in injected dose. Spatial resolution in MBI is dependent on lesion depth but independent of lesion location with MBIT. Reconstructed MBIT spatial resolution is substantially better than that in the projection images, suggesting future use of higher sensitivity collimators for even further reductions in injected activity.</p>","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":"7361 ","pages":"300-307"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5565232/pdf/nihms424638.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35441154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detective Quantum Efficiency of a CsI-CMOS X-ray Detector for Breast Tomosynthesis Operating in High Dynamic Range and High Sensitivity Modes.","authors":"Tushita Patel,&nbsp;Kelly Klanian,&nbsp;Zongyi Gong,&nbsp;Mark B Williams","doi":"10.1007/978-3-642-31271-7_11","DOIUrl":"https://doi.org/10.1007/978-3-642-31271-7_11","url":null,"abstract":"<p><p>The spatial frequency dependent detective quantum efficiency (DQE) of a CsI-CMOS x-ray detector was measured in two operating modes: a high dynamic range (HDR) mode and a high sensitivity (HS) mode. DQE calculations were performed using the IEC-62220-1-2 Standard. For detector entrance air kerma values between ~7 µGy and 60 µGy the DQE is similar in either HDR mode or HS mode, with a value of ~0.7 at low frequency and ~ 0.15 - 0.20 at the Nyquist frequency f_N = 6.7 mm^-1. In HDR mode the DQE remains virtually constant for operation with K_a values between ~7 µGy and 119 µGy but decreases for K_a levels below ~ 7 µGy. In HS mode the DQE is approximately constant over the full range of entrance air kerma tested between 1.7 µGy and 60 µGy but kerma values above ~75 µGy produce hard saturation. Quantum limited operation in HS mode for entrance kerma as small as 1.7 µGy makes it possible to use a large number of low dose views to improve angular sampling and decrease acquisition time.</p>","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":"7361 ","pages":"80-87"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-642-31271-7_11","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35312074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}