Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd

The effects of stereo shift angle, geometric magnification and display zoom on depth measurements in digital stereomammography

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134800/1/mp7615.pd

Effects of magnification and zooming on depth perception in digital stereomammography: an observer performance study

We are evaluating the application of stereoscopic imaging to digital mammography. In the current study, we investigated the effects of magnification and zooming on depth perception. A modular phantom was designed which contained six layers of 1-mm-thick Lexan plates, each spaced 1 mm apart. Eight to nine small, thin nylon fibrils were pasted on each plate in horizontal or vertical orientations such that they formed 25 crossing fibril pairs in a projected image. The depth separation between each fibril pair ranged from 2 to 10 mm. A change in the order of the Lexan plates changed the depth separation of the two fibrils in a pair. Stereoscopic image pairs of the phantom were acquired with a GE full-field digital mammography system. Three different phantom configurations were imaged. All images were obtained using a Rh target/Rh filter spectrum at 30 kVp tube potential and a ±3° stereo shift angle. Images were acquired in both contact and 1.8X magnification geometry and an exposure range of 4 to 63 mAs was employed. The images were displayed on a Barco monitor driven by a Metheus stereo graphics board and viewed with LCD stereo glasses. Five observers participated in the study. Each observer visually judged whether the vertical fibril was in front of or behind the horizontal fibril in each fibril pair. It was found that the accuracy of depth discrimination increased with increasing fibril depth separation and x-ray exposure. The accuracy was not improved by electronic display zooming of the contact stereo images by 2X. Under conditions of high noise (low mAs) and small depth separation between the fibrils, the observers' depth discrimination ability was significantly better in stereo images acquired with geometric magnification than in images acquired with a contact technique and displayed with or without zooming. Under our experimental conditions, a 2 mm depth discrimination was achieved with over 60% accuracy on contact images with and without zooming, and with over 90% accuracy on magnification images. This study indicates that stereoscopic imaging, especially with magnification, may be useful for visualizing the spatial distribution of microcalcifications in a cluster and for differentiating overlapping tissues from masses on mammograms.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48976/2/pmb3_22_007.pd

Aromatase inhibitor-induced modulation of breast density: clinical and genetic effects

Background: Change in breast density may predict outcome of women receiving adjuvant hormone therapy for breast cancer. We performed a prospective clinical trial to evaluate the impact of inherited variants in genes involved in oestrogen metabolism and signalling on change in mammographic percent density (MPD) with aromatase inhibitor (AI) therapy. Methods: Postmenopausal women with breast cancer who were initiating adjuvant AI therapy were enrolled onto a multicentre, randomised clinical trial of exemestane vs letrozole, designed to identify associations between AI-induced change in MPD and single-nucleotide polymorphisms in candidate genes. Subjects underwent unilateral craniocaudal mammography before and following 24 months of treatment. Results: Of the 503 enrolled subjects, 259 had both paired mammograms at baseline and following 24 months of treatment and evaluable DNA. We observed a statistically significant decrease in mean MPD from 17.1 to 15.1% (P<0.001), more pronounced in women with baseline MPD ⩾20%. No AI-specific difference in change in MPD was identified. No significant associations between change in MPD and inherited genetic variants were observed. Conclusion: Subjects with higher baseline MPD had a greater average decrease in MPD with AI therapy. There does not appear to be a substantial effect of inherited variants in biologically selected candidate genes

Image quality of microcalcifications in digital breast tomosynthesis: Effects of projection-view distributions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98731/1/MPH005703.pd

Breast Mass Characterization Using 3‐Dimensional Automated Ultrasound as an Adjunct to Digital Breast Tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135628/1/jum201332193.pd

Computerized characterization of breast masses on threeâ dimensional ultrasound volumes

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134860/1/mp9531.pd

Computer-aided detection system for clustered microcalcifications: comparison of performance on full-field digital mammograms and digitized screen-film mammograms

We have developed a computer-aided detection (CAD) system to detect clustered microcalcifications automatically on full-field digital mammograms (FFDMs) and a CAD system for screen-film mammograms (SFMs). The two systems used the same computer vision algorithms but their false positive (FP) classifiers were trained separately with sample images of each modality. In this study, we compared the performance of the CAD systems for detection of clustered microcalcifications on pairs of FFDM and SFM obtained from the same patient. For case-based performance evaluation, the FFDM CAD system achieved detection sensitivities of 70%, 80% and 90% at an average FP cluster rate of 0.07, 0.16 and 0.63 per image, compared with an average FP cluster rate of 0.15, 0.38 and 2.02 per image for the SFM CAD system. The difference was statistically significant with the alternative free-response receiver operating characteristic (AFROC) analysis. When evaluated on data sets negative for microcalcification clusters, the average FP cluster rates of the FFDM CAD system were 0.04, 0.11 and 0.33 per image at detection sensitivity level of 70%, 80% and 90% compared with an average FP cluster rate of 0.08, 0.14 and 0.50 per image for the SFM CAD system. When evaluated for malignant cases only, the difference of the performance of the two CAD systems was not statistically significant with AFROC analysis.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58099/2/pmb7_4_008.pd

Computer-aided assessment of diagnostic images for epidemiological research

<p>Abstract</p> <p>Background</p> <p>Diagnostic images are often assessed for clinical outcomes using subjective methods, which are limited by the skill of the reviewer. Computer-aided diagnosis (CAD) algorithms that assist reviewers in their decisions concerning outcomes have been developed to increase sensitivity and specificity in the clinical setting. However, these systems have not been well utilized in research settings to improve the measurement of clinical endpoints. Reductions in bias through their use could have important implications for etiologic research.</p> <p>Methods</p> <p>Using the example of cortical cataract detection, we developed an algorithm for assisting a reviewer in evaluating digital images for the presence and severity of lesions. Available image processing and statistical methods that were easily implementable were used as the basis for the CAD algorithm. The performance of the system was compared to the subjective assessment of five reviewers using 60 simulated images. Cortical cataract severity scores from 0 to 16 were assigned to the images by the reviewers and the CAD system, with each image assessed twice to obtain a measure of variability. Image characteristics that affected reviewer bias were also assessed by systematically varying the appearance of the simulated images.</p> <p>Results</p> <p>The algorithm yielded severity scores with smaller bias on images where cataract severity was mild to moderate (approximately ≤ 6/16^<it>ths</it>). On high severity images, the bias of the CAD system exceeded that of the reviewers. The variability of the CAD system was zero on repeated images but ranged from 0.48 to 1.22 for the reviewers. The direction and magnitude of the bias exhibited by the reviewers was a function of the number of cataract opacities, the shape and the contrast of the lesions in the simulated images.</p> <p>Conclusion</p> <p>CAD systems are feasible to implement with available software and can be valuable when medical images contain exposure or outcome information for etiologic research. Our results indicate that such systems have the potential to decrease bias and discriminate very small changes in disease severity. Simulated images are a tool that can be used to assess performance of a CAD system when a gold standard is not available.</p