Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its appli... more Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the 'knowledge' learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p = 0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.
Purpose: The authors are developing a computerized system for bladder segmentation in CT urogra-p... more Purpose: The authors are developing a computerized system for bladder segmentation in CT urogra-phy (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.
Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosyn-the... more Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosyn-thesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. Methods: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Results: Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). Conclusions: The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality. C 2016 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4967345]
With IRB approval, digital breast tomosynthesis (DBT) images of human subjects were collected usi... more With IRB approval, digital breast tomosynthesis (DBT) images of human subjects were collected using a GE GEN2 DBT prototype system. Corresponding digital mammograms (DMs) of the same subjects were collected retrospectively from patient files. The data set contained a total of 237 views of DBT and equal number of DM views from 120 human subjects, each included 163 views with microcalcification clusters (MCs) and 74 views without MCs. The data set was separated into training and independent test sets. The pre-processing, object prescreening and segmentation, false positive reduction and clustering strategies for MC detection by three computer-aided detection (CADe) systems designed for DM, DBT, and a planar projection image generated from DBT were analyzed. Receiver operating characteristic (ROC) curves based on features extracted from microcalcifications and free-response ROC (FROC) curves based on scores from MCs were used to quantify the performance of the systems. Jackknife FROC (JAFROC) and non-parametric analysis methods were used to determine the statistical difference between the FROC curves. The difference between the CAD DM and CAD DBT systems when the false positive rate was estimated from cases without MCs did not reach statistical significance. The study indicates that the large search space in DBT may not be a limiting factor for CADe to achieve similar performance as that observed in DM.
ABSTRACT PURPOSE Both mass and microcalcifications (MCs) are important signs for breast cancer. M... more ABSTRACT PURPOSE Both mass and microcalcifications (MCs) are important signs for breast cancer. Most regularization methods depend on local gradient and may treat the ill-defined margins or subtle spiculations of masses and subtle MCs as noise because of their small gradient. In this study, we developed a new bilateral-filter regularization method utilizing the multiscale structure (MBiF) of the image to enhance subtle edges and MCs in DBT. METHOD AND MATERIALS DBT data were first decomposed into frequency bands using the Laplacian Pyramid Decomposition. The mass margins and spiculations fall into the low-frequency (LF) bands while MCs, edges and noise fall into the high-frequency (HF) bands. Bilateral filtering with domain and range filters optimized for subtle signals was applied to the HF bands to exploit simultaneously the spatial and gray level information for enhancing the signals while suppressing noise. The image was reconstructed from the Laplacian pyramid after bilateral regularization in selected frequency bands. With IRB approval, DBT of subjects with MCs or masses was acquired with a GE prototype DBT system. DBT was reconstructed with SART regularized by MBiF and the total p-norm variation (TpV) method. The full width at half maximum (FWHM) of the gray-level profile of MCs or spiculations and the contrast-to-noise ratio (CNR) of MCs were used as sharpness and contrast measures to compare the two methods and without regularization (NR). RESULTS MBiF reduced spurious noise and contouring artifacts compared to TpV, thus preserving the image quality of the structured background. MBiF achieved 100-200% higher CNR for large MCs and 50-100% higher CNR for subtle MCs than NR, and 50-100% higher CNR than TpV for all MCs. The FWHMs of MCs were comparable among different methods. For spiculated masses, TpV blurred the spiculations and margins while MBiF preserved the edge sharpness and enhanced the mass margins. The FWHM of subtle spiculations by TpV was 25% larger than those by MBiF and NR. CONCLUSION The new MBiF method enhanced the CNR of MCs and preserved the sharpness of MCs and spiculated masses. MBiF provided better image quality of the structured background and was superior to TpV and NR for both MCs and masses. CLINICAL RELEVANCE/APPLICATION A regularized DBT reconstruction method that reduces noise and enhances the visibility of both MCs and masses may improve breast cancer detection and patient care without increasing dose.
Medical Imaging 2013: Computer-Aided Diagnosis, 2013
ABSTRACT We are developing a CAD system to assist radiologists in detecting microcalcification cl... more ABSTRACT We are developing a CAD system to assist radiologists in detecting microcalcification clusters (MCs) in digital breast tomosynthesis (DBT). In this study, we investigated the feasibility of using as input to the CAD system an enhanced DBT volume that was reconstructed with the iterative simultaneous algebraic reconstruction technique (SART) regularized by a new multiscale bilateral filtering (MBiF) method. The MBiF method utilizes the multiscale structures of the breast to selectively enhance MCs and preserve mass spiculations while smoothing noise in the DBT images. The CAD system first extracted the enhancement-modulated calcification response (EMCR) in the DBT volume. Detection of the seed points for MCs and individual calcifications were guided by the EMCR. MC candidates were formed by dynamic clustering. FPs were further reduced by analysis of the feature characteristics of the MCs. With IRB approval, two-view DBT of 91 subjects with biopsy-proven MCs were collected. Seventy-eight views from 39 subjects with MCs were used for training and the remaining 52 cases were used for independent testing. For view-based detection, a sensitivity of 85% was achieved at 3.23 FPs/volume. For case-based detection, the same sensitivity was obtained at 1.63 FPs/volume. The results indicate that the new MBiF method is useful in improving the detection accuracy of clustered microcalcifications. An effective CAD system for microcalcification detection in DBT has the potential to eliminate the need for additional mammograms, thereby reducing patient dose and reading time.
ABSTRACT Texture features of histopathological images of lung carcinoma have been evaluated using... more ABSTRACT Texture features of histopathological images of lung carcinoma have been evaluated using gray level co-occurrence matrices and multiwavelets. The investigation is done from a pathological perspective resulting in optimum subset of features for classification.
This paper describes a new approach to detection of microcalcification clusters (MCs) in digital ... more This paper describes a new approach to detection of microcalcification clusters (MCs) in digital breast tomosynthesis (DBT) via its planar projection (PPJ) image. With IRB approval, two-view (cranio-caudal and mediolateral oblique views) DBTs of human subject breasts were obtained with a GE GEN2 prototype DBT system that acquires 21 projection angles spanning 600 in 30 increments. A data set of 307 volumes (154 human subjects) was divided by case into independent training (127 with MCs) and test sets (104 with MCs and 76 free of MCs). Simultaneous algebraic reconstruction technique with multiscale bilateral filtering (MSBF) regularization was used to enhance microcalcifications and suppress noise. During the MSBF regularized reconstruction, the DBT volume was separated into high frequency (HF) and low frequency components representing microcalcifications and larger structures. At the final iteration, maximum intensity projection was applied to the regularized HF volume to generate a PPJ image that contained MCs with increased contrast-to-noise ratio (CNR) and reduced search space. High CNR objects in the PPJ image were extracted and labeled as microcalcification candidates. Convolution neural network (CNN) trained to recognize the image pattern of microcalcifications was used to classify the candidates into true calcifications and tissue structures and artifacts. The remaining microcalcification candidates were grouped into MCs by dynamic conditional clustering based on adaptive CNR threshold and radial distance criteria. False positive (FP) clusters were further reduced using the number of candidates in a cluster, CNR and size of microcalcification candidates. At 85% sensitivity an FP rate of 0.71 and 0.54 was achieved for view- and case-based sensitivity, respectively, compared to 2.16 and 0.85 achieved in DBT. The improvement was significant (p-value = 0.003) by JAFROC analysis.
Digital breast tomosynthesis (DBT) has the potential to replace digital mammography (DM) for brea... more Digital breast tomosynthesis (DBT) has the potential to replace digital mammography (DM) for breast cancer screening. An effective computer-aided detection (CAD) system for microcalcification clusters (MCs) on DBT will facilitate the transition. In this study, we collected a data set with corresponding DBT and DM for the same breasts. DBT was acquired with IRB approval and informed consent using a GE GEN2 DBT prototype system. The DM acquired with a GE Essential system for the patient’s clinical care was collected retrospectively from patient files. DM-based CAD (CADDM) and DBT-based CAD (CADDBT) were previously developed by our group. The major differences between the CAD systems include: (a) CADDBT uses two parallel processes whereas CADDM uses a single process for enhancing MCs and removing the structured background, (b) CADDBT has additional processing steps to reduce the false positives (FPs), including ranking of candidates of cluster seeds and cluster members and the use of adaptive CNR and size thresholds at clustering and FP reduction, (c) CADDM uses convolution neural network (CNN) and linear discriminant analysis (LDA) to differentiate true microcalcifications from FPs based on their morphological and CNN features. The performance difference is assessed by FROC analysis using test set (100 views with MCs and 74 views without MCs) independent of their respective training sets. At sensitivities of 70% and 80%, CADDBT achieved FP rates of 0.78 and 1.57 per view compared to 0.66 and 2.10 per image for the CADDM. JAFROC showed no significant difference between MC detection on DM and DBT by the two CAD systems.
Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its appli... more Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the 'knowledge' learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p = 0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.
Purpose: The authors are developing a computerized system for bladder segmentation in CT urogra-p... more Purpose: The authors are developing a computerized system for bladder segmentation in CT urogra-phy (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.
Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosyn-the... more Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosyn-thesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. Methods: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Results: Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). Conclusions: The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality. C 2016 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4967345]
With IRB approval, digital breast tomosynthesis (DBT) images of human subjects were collected usi... more With IRB approval, digital breast tomosynthesis (DBT) images of human subjects were collected using a GE GEN2 DBT prototype system. Corresponding digital mammograms (DMs) of the same subjects were collected retrospectively from patient files. The data set contained a total of 237 views of DBT and equal number of DM views from 120 human subjects, each included 163 views with microcalcification clusters (MCs) and 74 views without MCs. The data set was separated into training and independent test sets. The pre-processing, object prescreening and segmentation, false positive reduction and clustering strategies for MC detection by three computer-aided detection (CADe) systems designed for DM, DBT, and a planar projection image generated from DBT were analyzed. Receiver operating characteristic (ROC) curves based on features extracted from microcalcifications and free-response ROC (FROC) curves based on scores from MCs were used to quantify the performance of the systems. Jackknife FROC (JAFROC) and non-parametric analysis methods were used to determine the statistical difference between the FROC curves. The difference between the CAD DM and CAD DBT systems when the false positive rate was estimated from cases without MCs did not reach statistical significance. The study indicates that the large search space in DBT may not be a limiting factor for CADe to achieve similar performance as that observed in DM.
ABSTRACT PURPOSE Both mass and microcalcifications (MCs) are important signs for breast cancer. M... more ABSTRACT PURPOSE Both mass and microcalcifications (MCs) are important signs for breast cancer. Most regularization methods depend on local gradient and may treat the ill-defined margins or subtle spiculations of masses and subtle MCs as noise because of their small gradient. In this study, we developed a new bilateral-filter regularization method utilizing the multiscale structure (MBiF) of the image to enhance subtle edges and MCs in DBT. METHOD AND MATERIALS DBT data were first decomposed into frequency bands using the Laplacian Pyramid Decomposition. The mass margins and spiculations fall into the low-frequency (LF) bands while MCs, edges and noise fall into the high-frequency (HF) bands. Bilateral filtering with domain and range filters optimized for subtle signals was applied to the HF bands to exploit simultaneously the spatial and gray level information for enhancing the signals while suppressing noise. The image was reconstructed from the Laplacian pyramid after bilateral regularization in selected frequency bands. With IRB approval, DBT of subjects with MCs or masses was acquired with a GE prototype DBT system. DBT was reconstructed with SART regularized by MBiF and the total p-norm variation (TpV) method. The full width at half maximum (FWHM) of the gray-level profile of MCs or spiculations and the contrast-to-noise ratio (CNR) of MCs were used as sharpness and contrast measures to compare the two methods and without regularization (NR). RESULTS MBiF reduced spurious noise and contouring artifacts compared to TpV, thus preserving the image quality of the structured background. MBiF achieved 100-200% higher CNR for large MCs and 50-100% higher CNR for subtle MCs than NR, and 50-100% higher CNR than TpV for all MCs. The FWHMs of MCs were comparable among different methods. For spiculated masses, TpV blurred the spiculations and margins while MBiF preserved the edge sharpness and enhanced the mass margins. The FWHM of subtle spiculations by TpV was 25% larger than those by MBiF and NR. CONCLUSION The new MBiF method enhanced the CNR of MCs and preserved the sharpness of MCs and spiculated masses. MBiF provided better image quality of the structured background and was superior to TpV and NR for both MCs and masses. CLINICAL RELEVANCE/APPLICATION A regularized DBT reconstruction method that reduces noise and enhances the visibility of both MCs and masses may improve breast cancer detection and patient care without increasing dose.
Medical Imaging 2013: Computer-Aided Diagnosis, 2013
ABSTRACT We are developing a CAD system to assist radiologists in detecting microcalcification cl... more ABSTRACT We are developing a CAD system to assist radiologists in detecting microcalcification clusters (MCs) in digital breast tomosynthesis (DBT). In this study, we investigated the feasibility of using as input to the CAD system an enhanced DBT volume that was reconstructed with the iterative simultaneous algebraic reconstruction technique (SART) regularized by a new multiscale bilateral filtering (MBiF) method. The MBiF method utilizes the multiscale structures of the breast to selectively enhance MCs and preserve mass spiculations while smoothing noise in the DBT images. The CAD system first extracted the enhancement-modulated calcification response (EMCR) in the DBT volume. Detection of the seed points for MCs and individual calcifications were guided by the EMCR. MC candidates were formed by dynamic clustering. FPs were further reduced by analysis of the feature characteristics of the MCs. With IRB approval, two-view DBT of 91 subjects with biopsy-proven MCs were collected. Seventy-eight views from 39 subjects with MCs were used for training and the remaining 52 cases were used for independent testing. For view-based detection, a sensitivity of 85% was achieved at 3.23 FPs/volume. For case-based detection, the same sensitivity was obtained at 1.63 FPs/volume. The results indicate that the new MBiF method is useful in improving the detection accuracy of clustered microcalcifications. An effective CAD system for microcalcification detection in DBT has the potential to eliminate the need for additional mammograms, thereby reducing patient dose and reading time.
ABSTRACT Texture features of histopathological images of lung carcinoma have been evaluated using... more ABSTRACT Texture features of histopathological images of lung carcinoma have been evaluated using gray level co-occurrence matrices and multiwavelets. The investigation is done from a pathological perspective resulting in optimum subset of features for classification.
This paper describes a new approach to detection of microcalcification clusters (MCs) in digital ... more This paper describes a new approach to detection of microcalcification clusters (MCs) in digital breast tomosynthesis (DBT) via its planar projection (PPJ) image. With IRB approval, two-view (cranio-caudal and mediolateral oblique views) DBTs of human subject breasts were obtained with a GE GEN2 prototype DBT system that acquires 21 projection angles spanning 600 in 30 increments. A data set of 307 volumes (154 human subjects) was divided by case into independent training (127 with MCs) and test sets (104 with MCs and 76 free of MCs). Simultaneous algebraic reconstruction technique with multiscale bilateral filtering (MSBF) regularization was used to enhance microcalcifications and suppress noise. During the MSBF regularized reconstruction, the DBT volume was separated into high frequency (HF) and low frequency components representing microcalcifications and larger structures. At the final iteration, maximum intensity projection was applied to the regularized HF volume to generate a PPJ image that contained MCs with increased contrast-to-noise ratio (CNR) and reduced search space. High CNR objects in the PPJ image were extracted and labeled as microcalcification candidates. Convolution neural network (CNN) trained to recognize the image pattern of microcalcifications was used to classify the candidates into true calcifications and tissue structures and artifacts. The remaining microcalcification candidates were grouped into MCs by dynamic conditional clustering based on adaptive CNR threshold and radial distance criteria. False positive (FP) clusters were further reduced using the number of candidates in a cluster, CNR and size of microcalcification candidates. At 85% sensitivity an FP rate of 0.71 and 0.54 was achieved for view- and case-based sensitivity, respectively, compared to 2.16 and 0.85 achieved in DBT. The improvement was significant (p-value = 0.003) by JAFROC analysis.
Digital breast tomosynthesis (DBT) has the potential to replace digital mammography (DM) for brea... more Digital breast tomosynthesis (DBT) has the potential to replace digital mammography (DM) for breast cancer screening. An effective computer-aided detection (CAD) system for microcalcification clusters (MCs) on DBT will facilitate the transition. In this study, we collected a data set with corresponding DBT and DM for the same breasts. DBT was acquired with IRB approval and informed consent using a GE GEN2 DBT prototype system. The DM acquired with a GE Essential system for the patient’s clinical care was collected retrospectively from patient files. DM-based CAD (CADDM) and DBT-based CAD (CADDBT) were previously developed by our group. The major differences between the CAD systems include: (a) CADDBT uses two parallel processes whereas CADDM uses a single process for enhancing MCs and removing the structured background, (b) CADDBT has additional processing steps to reduce the false positives (FPs), including ranking of candidates of cluster seeds and cluster members and the use of adaptive CNR and size thresholds at clustering and FP reduction, (c) CADDM uses convolution neural network (CNN) and linear discriminant analysis (LDA) to differentiate true microcalcifications from FPs based on their morphological and CNN features. The performance difference is assessed by FROC analysis using test set (100 views with MCs and 74 views without MCs) independent of their respective training sets. At sensitivities of 70% and 80%, CADDBT achieved FP rates of 0.78 and 1.57 per view compared to 0.66 and 2.10 per image for the CADDM. JAFROC showed no significant difference between MC detection on DM and DBT by the two CAD systems.
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