Development of an automated detection algorithm for patient motion blur in digital mammograms

The purpose is to develop and validate an automated method for detecting image unsharpness caused by patient motion blur in digital mammograms. The goal is that such a tool would facilitate immediate re-taking of blurred images, which has the potential to reduce the number of recalled examinations, and to ensure that sharp, high-quality mammograms are presented for reading. To meet this goal, an automated method was developed based on interpretation of the normalized image Wiener Spectrum. A preliminary algorithm was developed using 25 cases acquired using a single vendor system, read by two expert readers identifying the presence of blur, location, and severity. A predictive blur severity score was established using multivariate modeling, which had an adjusted coefficient of determination, R2 =0.63±0.02, for linear regression against the average reader-scored blur severity. A heatmap of the relative blur magnitude showed good correspondence with reader sketches of blur location, with a Spearman rank correlation of 0.70 between the algorithmestimated area fraction with blur and the maximum of the blur area fraction categories of the two readers. Given these promising results, the algorithm-estimated blur severity score and heatmap are proposed to be used to aid observer interpretation. The use of this automated blur analysis approach, ideally with feedback during an exam, could lead to a reduction in repeat appointments for technical reasons, saving time, cost, potential anxiety, and improving image quality for accurate diagnosis.</p

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

Automatic quality assessment in mammography screening:a deep learning based segmentation method

Abstract. Mammography is an imaging method used as a main tool to detect breast cancer at early stages. Images (mammograms) are examined by radiologists, who aim to identify cancerous findings. However, in order to do that, the mammograms need to be of diagnostic quality, which can sometimes be insufficient, and thus the quality of diagnosis also suffers. Radiology technicians (radiographers) are trained to take mammography images, but not in every healthcare center a strict quality control process is established, which may substantially affect the patients. The most common defects in mammograms are positioning defects, which are seen in the images as skin-foldings or non-imaged parts of the breast. The major issue at a process level is that the described positioning issues are noticed late, already at the diagnostic phase. If a radiologist decides that the mammogram is a non-diagnostic quality, the patient needs to revisit the imaging center. If quality control could be automated and standardized, unnecessary patient recalls could be avoided, thus, reducing the costs of the mammographic process. To date, there is a lack of automatic general quality control tools for mammography screening. Looking at the recent advances in artificial intelligence, it may be possible to automate this process. The goal of this thesis was to develop an automatic system for quality assessment of mammograms. The author used Deep learning to develop an automatic framework for automatic segmentation of defects in mammograms using a dataset of 512 mammographic images extracted from the Oulu University Hospital archive. The second stage of the developed method performed quality assessment by analyzing the presence and location of different tissues in the images from the predicted segmentations. The developed segmentation model yielded a Dice coefficient over 0.90 for the whole breast, breast, and pectoral muscle, and over 0.60 for skin-foldings and nipple. The developed method is the first to tackle automatic segmentation of all major positioning issues in mammography. Ultimately, the developed technology has a potential to improve the mammography workflows and, eventually, patient outcomes.Automaattinen laadunarviointi mammografian kuvauksessa : syväoppimispohjainen segmentointimenetelmä. Tiivistelmä. Mammografiaa on kuvantamismenetelmä, jota käytetään päävälineenä rintasyövän havaitsemiseksi varhaisessa vaiheessa. Radiologien on tutkittava mammogrammit ja päätettävä sitten, onko pahanlaatusia löydöksiä, ja tätä varten mammografiakuvien on oltava diagnostisesti laadukkaita. Ammattilaiset koulutetaan mammografiakuvien ottamiseksi, mutta ei kaikissa terveyskeskuksissa on otettu käyttöön tiukka laadunvalvontaprosessi, joka voi vaikuttaa merkittävästi potilaisiin. Kuvissa voi olla virheitä, jotka tekevät kuvista ei-diagnostisen laadukkaan mammogrammin, ja ne voivat vaikuttaa diagnostiikkatuloksiin. Yksi näistä vioista ovat paikannusvirheet, joissa näkyvät kuvissa ihon taitoksina ja jotkut rinnan osat eivät näy. Suurin ongelma prosessitasolla on, että kuvatut paikannusvirheet havaitaan myöhässä, jo diagnoosivaiheessa. Jos radiologit päättävät, että mammografiakuva ei ole diagnostisesti laadukas, potilaan on palattava kuvantamiskeskukseen ja tutkittava uudelleen, mikä voi lisätä kustannuksia ja työmäärää. Jos laadunvalvonta voidaan automatisoida ja standardoida, voidaan välttää tarpeetonta potilaan palauttamista ja vähentää siten mammografiaprosessin kustannuksia. Tähän mennessä mammografiaseulonnassa ei ole automaattista yleistä laadunvalvontaa. Kun tarkastellaan tekoälyn viimeaikaisia edistystä, tämän prosessin automatisointi voi olla mahdollista. Tämän projektin tarkoituksena oli todistaa diagnostisten ja ei-diagnostisten laatumammogrammien automaattisen erottamisen toteutettavuus. Kirjoittaja käytti syvää oppimista automatisoidun kehyksen luomisessa käyttämällä 512 mammografiakuvaa, jotka otettiin Oulun yliopistollisen sairaalan arkistosta. Automaattisen menetelmän ensimmäisessä vaiheessa suoritettiin rintakudosten ja ihon taittumien segmentointi. Toisessa vaiheessa suoritettiin laadunarviointi analysoimalla eri kudosten läsnäolo ja sijainti kuvissa. Kehitetyllä segmentointimallilla saavutettiin merkittäviä tuloksia, kun koko rinnan ja rintalihasten segmentoinnin onnistumisen hyvyttä mittaava Dice-kerroin oli yli 0,90, ja ihon taittumiselle ja nännille yli 0,60. Kehitetty menetelmä on ensimmäinen, joka käsittelee mammografian kaikkien tärkeimpien paikannusvirheiden automaattista segmentointia. Sillä on potentiaalia myötävaikuttaa mammografian työnkulkujen ja potilastulosten parantamiseen

Computerâ aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134897/1/mp2072.pd

Computer aided detection in mammography

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Deformable mapping technique to correlate lesions in digital breast tomosynthesis and automated breast ultrasound images

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146324/1/mp13113.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146324/2/mp13113_am.pd

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Enhanced algorithms for lesion detection and recognition in ultrasound breast images

Mammography is the gold standard for breast cancer detection. However, it has very high false positive rates and is based on ionizing radiation. This has led to interest in using multi-modal approaches. One modality is diagnostic ultrasound, which is based on non-ionizing radiation and picks up many of the cancers that are generally missed by mammography. However, the presence of speckle noise in ultrasound images has a negative effect on image interpretation. Noise reduction, inconsistencies in capture and segmentation of lesions still remain challenging open research problems in ultrasound images. The target of the proposed research is to enhance the state-of-art computer vision algorithms used in ultrasound imaging and to investigate the role of computer processed images in human diagnostic performance. [Continues.