Digital breast tomosynthesis: computer-aided detection of clustered microcalcifications on planar projection images

Samala, Ravi K.; Chan, Heang Ping; Lu, Yao; Hadjiiski, Lubomir M.; Wei, Jun; Helvie, Mark A.

doi:10.1088/0031-9155/59/23/7457

Cited by 31 publications

(28 citation statements)

References 49 publications

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“…Convolutional neural networks (CNNs) have been used previously to classify patterns in medical images for use with computer-aided detection and specifically for microcalcification detection in mammograms. [19][20][21][22][23][24][25][26] In these applications, the training sets were typically small, generally using less than 500 samples. As computational power grows, CNNs with very complex architectures that require training with massive data become practical.…”

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confidence: 99%

Urinary bladder segmentation in CT urography using deep‐learning convolutional neural network and level sets

et al. 2016

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Purpose: The authors are developing a computerized system for bladder segmentation in CT urography (CTU) as a critical component for computer-aided detection of bladder cancer. Methods: A deep-learning convolutional neural network (DL-CNN) was trained to distinguish between the inside and the outside of the bladder using 160 000 regions of interest (ROI) from CTU images. The trained DL-CNN was used to estimate the likelihood of an ROI being inside the bladder for ROIs centered at each voxel in a CTU case, resulting in a likelihood map. Thresholding and hole-filling were applied to the map to generate the initial contour for the bladder, which was then refined by 3D and 2D level sets. The segmentation performance was evaluated using 173 cases: 81 cases in the training set (42 lesions, 21 wall thickenings, and 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, and 13 normal bladders). The computerized segmentation accuracy using the DL likelihood map was compared to that using a likelihood map generated by Haar features and a random forest classifier, and that using our previous conjoint level set analysis and segmentation system (CLASS) without using a likelihood map. All methods were evaluated relative to the 3D hand-segmented reference contours. Results: With DL-CNN-based likelihood map and level sets, the average volume intersection ratio, average percent volume error, average absolute volume error, average minimum distance, and the Jaccard index for the test set were 81.9% ± 12.1%, 10.2% ± 16.2%, 14.0% ± 13.0%, 3.6 ± 2.0 mm, and 76.2% ± 11.8%, respectively. With the Haar-feature-based likelihood map and level sets, the corresponding values were 74.3% ± 12.7%, 13.0% ± 22.3%, 20.5% ± 15.7%, 5.7 ± 2.6 mm, and 66.7% ± 12.6%, respectively. With our previous CLASS with local contour refinement (LCR) method, the corresponding values were 78.0% ± 14.7%, 16.5% ± 16.8%, 18.2% ± 15.0%, 3.8 ± 2.3 mm, and 73.9% ± 13.5%, respectively. Conclusions: The authors demonstrated that the DL-CNN can overcome the strong boundary between two regions that have large difference in gray levels and provides a seamless mask to guide level set segmentation, which has been a problem for many gradient-based segmentation methods. Compared to our previous CLASS with LCR method, which required two user inputs to initialize the segmentation, DL-CNN with level sets achieved better segmentation performance while using a single user input. Compared to the Haar-feature-based likelihood map, the DL-CNN-based likelihood map could guide the level sets to achieve better segmentation. The results demonstrate the feasibility of our new approach of using DL-CNN in combination with level sets for segmentation of the bladder. C 2016 American Association of Physicists in Medicine. [http://dx

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Urinary bladder segmentation in CT urography using deep‐learning convolutional neural network and level sets

et al. 2016

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“…The CAD systems for the 60°-21PV geometry based on the DBT reconstructed volume and the PPJ image were developed and described in detail previously (Samala et al , 2014b; Samala et al , 2014c). These CAD systems were applied to the 60°-21PV data set and the 30°-11PV data set.…”

Section: Methodsmentioning

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“…In our laboratory, a GE GEN2 prototype DBT system acquiring 21 PVs over a 60° tomographic angle was used for collecting DBT cases. We have been developing methods to detect MCs in DBT (Sahiner et al , 2012; Samala et al , 2014c), PVs (Wei et al , 2014), and PPJ image (Samala et al , 2014b). Our CAD systems achieved 85% sensitivity at 2.16 FPs/view in DBT volume, 90% sensitivity at 1.55 FPs/view in PVs, and 85% sensitivity at 0.71 FPs/view in PPJ images.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided detection system for clustered microcalcifications in digital breast tomosynthesis using joint information from volumetric and planar projection images

Samala

Chan

et al. 2015

Phys. Med. Biol.

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We propose a novel approach for detection of microcalcification clusters (MCs) using joint information from digital breast tomosynthesis (DBT) volume and planar projection (PPJ) image. A data set of 307 DBT views was collected with IRB approval using a prototype DBT system. The system acquires 21 projection views (PVs) from a wide tomographic angle of 60° (60°-21PV) at about twice the dose of a digital mammography (DM) system, which allows us the flexibility of simulating other DBT acquisition geometries using a subset of the PVs. In this study, we simulated a 30° DBT geometry using the central 11 PVs (30°-11PV). The narrower tomographic angle is closer to DBT geometries commercially available or under development and the dose is matched approximately to that of a DM. We developed a new joint-CAD system for detection of clustered microcalcifications. The DBT volume was reconstructed with a multiscale bilateral filtering regularized method and a PPJ image was generated from the reconstructed volume. Task-specific detection strategies were designed to combine information from the DBT volume and the PPJ image. The data set was divided into a training set (127 views with MCs) and an independent test set (104 views with MCs and 76 views without MCs). The joint-CAD system outperformed the individual CAD systems for DBT volume or PPJ image alone; the differences in the test performances were statistically significant (p<0.05) using JAFROC analysis.

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“…A critical component of such a system is accurate segmentation of the bladders from the surrounding structures, which is a challenging problem because of the presence of the non-contrast and contrast-filled regions in the bladder and the low contrast boundaries between the bladder and the adjacent structures in the abdomen. Convolutional neural networks (CNN) have been used previously to classify patterns in medical images for use with computer-aided detection and specifically for microcalcification detection in mammograms [2][3][4][5][6][7][8][9][10] . In this study, we investigated the application of a deep-learning convolutional neural network (DL-CNN) by to bladder segmentation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of bladder segmentation using deep-learning convolutional neural network with and without level sets

et al. 2016

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We are developing a CAD system for detection of bladder cancer in CTU. In this study we investigated the application of deep-learning convolutional neural network (DL-CNN) to the segmentation of the bladder, which is a challenging problem because of the strong boundary between the non-contrast and contrast-filled regions in the bladder. We trained a DL-CNN to estimate the likelihood of a pixel being inside the bladder using neighborhood information. The segmented bladder was obtained from thresholding and hole-filling of the likelihood map. We compared the segmentation performance of the DL-CNN alone and with additional cascaded 3D and 2D level sets to refine the segmentation using 3D hand-segmented contours as reference standard. The segmentation accuracy was evaluated by five performance measures: average volume intersection %, average % volume error, average absolute % error, average minimum distance, and average Jaccard index for a data set of 81 training and 92 test cases. For the training set, DL-CNN with level sets achieved performance measures of 87.2±6.1%, 6.0±9.1%, 8.7±6.1%, 3.0±1.2 mm, and 81.9±7.6%, respectively, while the DL-CNN alone obtained the values of 73.6±8.5%, 23.0±8.5%, 23.0±8.5%, 5.1±1.5 mm, and 71.5±9.2%, respectively. For the test set, the DL-CNN with level sets achieved performance measures of 81.9±12.1%, 10.2±16.2%, 14.0±13.0%, 3.6±2.0 mm, and 76.2±11.8%, respectively, while DL-CNN alone obtained 68.7±12.0%, 27.2±13.7%, 27.4±13.6%, 5.7±2.2 mm, and 66.2±11.8%, respectively. DL-CNN alone is effective in segmenting bladders but may not follow the details of the bladder wall. The combination of DL-CNN with level sets provides highly accurate bladder segmentation.

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Digital breast tomosynthesis: computer-aided detection of clustered microcalcifications on planar projection images

Cited by 31 publications

References 49 publications

Urinary bladder segmentation in CT urography using deep‐learning convolutional neural network and level sets

Urinary bladder segmentation in CT urography using deep‐learning convolutional neural network and level sets

Computer-aided detection system for clustered microcalcifications in digital breast tomosynthesis using joint information from volumetric and planar projection images

Comparison of bladder segmentation using deep-learning convolutional neural network with and without level sets

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