Computer-aided image quality assessment in automated 3D breast ultrasound images

Automated 3D breast ultrasound (ABUS) is a valuable, non-ionising adjunct to X-ray mammography for breast cancer screening and diagnosis for women with dense breasts. High image quality is an important prerequisite for diagnosis and has to be guaranteed at the time of acquisition. The high throughput of images in a screening scenario demands for automated solutions. In this work, an automated image quality assessment system rating ABUS scans at the time of acquisition was designed and implemented. Quality assessment of present diagnostic ultrasound images has rarely been performed demanding thorough analysis of potential image quality aspects in ABUS. Therefore, a reader study was initiated, making two clinicians rate the quality of clinical ABUS images. The frequency of specific quality aspects was evaluated revealing that incorrect positioning and insufficiently applied contact fluid caused the most relevant image quality issues. The relative position of the nipple in the image, the acoustic shadow caused by the nipple as well as the shape of the breast contour reflect patient positioning and ultrasound transducer handling. Morphological and histogram-based features utilized for machine learning to reproduce the manual classification as provided by the clinicians. At 97 % specificity, the automatic classification achieved sensitivities of 59 %, 45 %, and 46 % for the three aforementioned aspects, respectively. The nipple is an important landmark in breast imaging, which is generally---but not always correctly---pinpointed by the technicians. An existing nipple detection algorithm was extended by probabilistic atlases and exploited for automatic detection of incorrectly annotated nipple marks. The nipple detection rate was increased from 82 % to 85 % and the classification achieved 90 % sensitivity at 89 % specificity. A lack of contact fluid between transducer and skin can induce reverberation patterns and acoustic shadows, which can possibly obscure lesions. Parameter maps were computed in order to localize these artefact regions and yielded a detection rate of 83 % at 2.6 false positives per image. Parts of the presented work were integrated to clinical workflow making up a novel image quality assessment system that supported technicians in their daily routine by detecting images of insufficient quality and indicating potential improvements for a repeated scan while the patient was still in the examination room. First evaluations showed that the proposed method sensitises technicians for the radiologists' demands on diagnostically valuable images

The Role Of Tissue Sound Speed As A Surrogate Marker Of Breast Density

Breast density is one of the strongest predictors of breast cancer risk as women with the densest breasts have a three- to five-fold increase in risk compared to women with the least dense breasts. Breast density is currently measured by using mammography, the current gold standard for breast imaging. There are many shortcomings to using mammography to measure breast density, including the use of ionizing radiation. Ultrasound tomography (UST) does not use ionizing radiation and can create tomographic breast sound speed images. These sound speed images are useful because breast density is proportional to sound speed. The purpose of this work was to assess the ability of UST to measure breast density and its ability to measure changes in breast density over short periods of time. A cohort of 251 patients was examined using both UST and mammography. Many different associations were found between the UST density measurement, the volume averaged sound speed, and the mammographic percent density. Additional associations were found between many other UST and mammographic imaging characteristics. UST density was found to correlate with various patient characteristics in a similar manner to mammographic density. Additionally, UST was used to examine the effects of tamoxifen on breast density. Tamoxifen has been shown to reduce mammographic density and breast cancer risk for some women. Preliminary data for 52 patients has shown promising results so far. UST density has decreased for approximately a similar percentage of patients as has been measured for mammographic density. These changes have been measured over short time frames that could not be achieved using mammography. These results show that UST\u27s ability to measure breast density is consistent with mammography, the current standard of care. UST has the potential to become a safe and effective device that can be used to reliably assess breast density and serial changes in breast density

Development of a Diagnostic Test Set to Assess Agreement in Breast Pathology: Practical Application of the Guidelines for Reporting Reliability and Agreement Studies (GRRAS)

Diagnostic test sets are a valuable research tool that contributes importantly to the validity and reliability of studies that assess agreement in breast pathology. In order to fully understand the strengths and weaknesses of any agreement and reliability study, however, the methods should be fully reported. In this paper we provide a step-by-step description of the methods used to create four complex test sets for a study of diagnostic agreement among pathologists interpreting breast biopsy specimens. We use the newly developed Guidelines for Reporting Reliability and Agreement Studies (GRRAS) as a basis to report these methods

Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images

Abstract Background The imaging assessment of paraspinal muscle morphology and fatty infiltration has gained considerable attention in the past decades, with reports suggesting an association between muscle degenerative changes and low back pain (LBP). To date, qualitative and quantitative approaches have been used to assess paraspinal muscle composition. Though highly reliable, manual thresholding techniques are time consuming and not always feasible in a clinical setting. The tedious and rater-dependent nature of such manual thresholding techniques provides the impetus for the development of automated or semi-automated segmentation methods. The purpose of the present study was to develop and evaluate an automated thresholding algorithm for the assessment of paraspinal muscle composition. The reliability and validity of the muscle measurements using the new automated thresholding algorithm were investigated through repeated measurements and comparison with measurements from an established, highly reliable manual thresholding technique. Methods Magnetic resonance images of 30 patients with LBP were randomly selected cohort of patients participating in a project on commonly diagnosed lumbar pathologies in patients attending spine surgeon clinics. A series of T2-weighted MR images were used to train the algorithm; preprocessing techniques including adaptive histogram equalization method image adjustment scheme were used to enhance the quality and contrast of the images. All muscle measurements were repeated twice using a manual thresholding technique and the novel automated thresholding algorithm, from axial T2-weigthed images, at least 5 days apart. The rater was blinded to all earlier measurements. Inter-method agreement and intra-rater reliability for each measurement method were assessed. The study did not received external funding and the authors have no disclosures. Results There was excellent agreement between the two methods with inter-method reliability coefficients (intraclass correlation coefficients) varying from 0.79 to 0.99. Bland and Altman plots further confirmed the agreement between the two methods. Intra-rater reliability and standard error of measurements were comparable between methods, with reliability coefficient varying between 0.95 and 0.99 for the manual thresholding and 0.97–0.99 for the automated algorithm. Conclusion The proposed automated thresholding algorithm to assess paraspinal muscle size and composition measurements was highly reliable, with excellent agreement with the reference manual thresholding method

A study to evaluate the reliability of using two-dimensional photographs, three-dimensional images, and stereoscopic projected three-dimensional images for patient assessment

Clinicians are accustomed to viewing conventional two-dimensional (2D) photographs and assume that viewing three-dimensional (3D) images is similar. Facial images captured in 3D are not viewed in true 3D; this may alter clinical judgement. The aim of this study was to evaluate the reliability of using conventional photographs, 3D images, and stereoscopic projected 3D images to rate the severity of the deformity in pre-surgical class III patients. Forty adult patients were recruited. Eight raters assessed facial height, symmetry, and profile using the three different viewing media and a 100-mm visual analogue scale (VAS), and appraised the most informative viewing medium. Inter-rater consistency was above good for all three media. Intra-rater reliability was not significantly different for rating facial height using 2D (P = 0.704), symmetry using 3D (P = 0.056), and profile using projected 3D (P = 0.749). Using projected 3D for rating profile and symmetry resulted in significantly lower median VAS scores than either 3D or 2D images (all P < 0.05). For 75% of the raters, stereoscopic 3D projection was the preferred method for rating. The reliability of assessing specific characteristics was dependent on the viewing medium. Clinicians should be aware that the visual information provided when viewing 3D images is not the same as when viewing 2D photographs, especially for facial depth, and this may change the clinical impression

The Empirical Foundations of Teleradiology and Related Applications: A Review of the Evidence

Introduction: Radiology was founded on a technological discovery by Wilhelm Roentgen in 1895. Teleradiology also had its roots in technology dating back to 1947 with the successful transmission of radiographic images through telephone lines. Diagnostic radiology has become the eye of medicine in terms of diagnosing and treating injury and disease. This article documents the empirical foundations of teleradiology. Methods: A selective review of the credible literature during the past decade (2005?2015) was conducted, using robust research design and adequate sample size as criteria for inclusion. Findings: The evidence regarding feasibility of teleradiology and related information technology applications has been well documented for several decades. The majority of studies focused on intermediate outcomes, as indicated by comparability between teleradiology and conventional radiology. A consistent trend of concordance between the two modalities was observed in terms of diagnostic accuracy and reliability. Additional benefits include reductions in patient transfer, rehospitalization, and length of stay.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140295/1/tmj.2016.0149.pd

Dual-intended deep learning model for breast cancer diagnosis in ultrasound imaging

Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is proposed. A convolutional deep autoencoder model is presented for simultaneous segmentation and radiomic extraction. The model segments the breast lesions while concurrently extracting radiomic features. With our deep model, we perform breast lesion segmentation, which is linked to low-dimensional deep-radiomic extraction (four features). Similarly, we used high dimensional conventional imaging throughputs and applied spectral embedding techniques to reduce its size from 354 to 12 radiomics. A total of 780 ultrasound images—437 benign, 210, malignant, and 133 normal—were used to train and validate the models in this study. To diagnose malignant lesions, we have performed training, hyperparameter tuning, crossvalidation, and testing with a random forest model. This resulted in a binary classification accuracy of 78.5% (65.1–84.1%) for the maximal (full multivariate) cross-validated model for a combination of radiomic groups

Beyond mammography : an evaluation of complementary modalities in breast imaging

Breast cancer is the main cause of cancer death among women worldwide and the goal of mammography screening is to reduce breast cancer-specific mortality. The reduction of the sensitivity of mammography for detecting cancer among women with dense breasts requires the use of complementary methods for this subset of women. Three of the projects in this thesis examine the performance of such complementary methods and a fourth study investigates the association between the biomarker BPE (background parenchymal enhancement) and risk factors for breast cancer. In study 1, we prospectively compared the sensitivity and specificity of Automated Breast Volume Scanner (ABVS) with handheld ultrasound for detection of breast cancer among women with a suspicious mammographic finding who were recalled after attending the population-based mammography screening program. We performed both methods on 113 women and found 26 malignant lesions. Analysis was performed in two categories: breasts with a suspicious screening mammography and breasts with a negative screening mammography. In the first category (n=118) the sensitivity of both methods was 88% (p=1.0), the specificity of handheld ultrasound was 93.5 % and ABVS was 89.2%. The difference in specificity was not statistically significant (p=0.29). For breasts without a suspicious mammographic finding, the sensitivity of handheld ultrasound and ABVS was 100% (p=1.0), the specificity was 100% and 94.1% respectively. The difference in specificity was statistically significant (p=0.03). In summary, ABVS has similar sensitivity to handheld ultrasound, but lower specificity in breasts with a negative mammogram. In study 2, we explored the incremental cancer detection rate when adding a threedimensional infrared imaging (3DIRI) score to screening mammography among women with dense breasts (Volpara volumetric density >6 % on the previous mammography examination) who attended the population-based mammography screening program. Women with a negative mammogram and positive 3DIRI score were triaged for a DCEMRI examination to verify the presence of cancer. Of 1727 participants, 7 women had a mammography-detected breast cancer. Among women with a negative mammogram and a positive infrared imaging (n=219), an additional 6 cancers in 5 women were detected on MRI resulting in an incremental cancer detection rate of 22.5 per 1000. Among women with a negative mammography and infrared examination, one woman was diagnosed with breast cancer during the two-year follow-up. The study does not provide information on the proportion of cancers that might have been detected had MRI been performed among women with a negative mammogram and 3DIRI score. Consequently, this study does not shed light on the diagnostic accuracy of infrared imaging or whether using an infrared risk score is the optimal method for identifying women who would benefit from additional imaging modalities. In study 3, we used MRI examinations of study 2 among women without breast cancer (n=214) to explore the association between BPE at DCE-MRI and a large array of risk factors for breast cancer. Thanks to the Karma database, we had unique access to data from self-reporting questionnaires on risk factors. BPE and mammographic density were assessed visually by three radiologists and BPE was further dichotomized into low and high. We created categorical variables for other risk factors. We calculated the univariable associations between BPE and each risk factor and fitted an adjusted logistic regression model. In the adjusted model, we found a negative association with age (p=0.002), and a positive association with BMI (p=0.03). There was a statistically significant association with systemic progesterone (p=0.03) but since only five participants used progesterone preparations, the result is uncertain. Although the likelihood for high BPE increased with increase in mammographic density, the association was not statistically significant (p=0.23). We were able to confirm earlier findings that BPE is associated with age, BMI and progesterone, but we could not find an association with other risk factors for breast cancer. In study 4, we compared the diagnostic accuracy, reading-time, and inter-rater agreement of an abbreviated protocol (aMRI) to the routine full protocol (fMRI) of contrast-enhanced breast MRI. The MRI examinations were performed before biopsy and among women who were not part of a surveillance program due to an increased familial risk of breast cancer. Analysis was performed on a per breast basis. Aggregated across three readers, the sensitivity and specificity were 93.0% and 91.7% for aMRI, and 92.0% and 94.3% for the fMRI. Using a generalized estimating equations approach to compare the two protocols, the difference in sensitivity was not statistically significant (p=0.840), and the difference in specificity was significant (p=0.003). There was a statistically significant difference in average reading time of 67 seconds for aMRI and 126 seconds for the fMRI (p= 0.000). The inter-rater agreement was 0.79 for aMRI and 0.83 for fMRI. We were able to demonstrate that the abbreviated protocol has similar sensitivity to the full protocol even if MRI is performed before biopsy and the images lack telltale signs of malignancy. In conclusion, this thesis provides new knowledge about the biomarker BPE, broadens our knowledge on the diagnostic accuracy of two different imaging modalities and highlights the importance of good study design for diagnostic accuracy studies