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

Study of breast implants mammography examinations for identification of suitable image quality criteria

Purpose: To characterise the mammography technique used in breast cancer screening programmes for breast implants (BI) and to identify if the image quality (IQ) criteria available in literature are applicable to BI imaging. Methods: The study was conducted in two phases: literature review to find IQ criteria used in mammography combining keywords in several sources; and assessment of 1207 BI mammograms using the criteria that was identified previously to see if they were achieved or not. An observation grid was used to collect information about positioning, beam energy, compression force, and exposure mode. Descriptive statistics and Student’s t test and χ2 test were performed according to the nature of the variables. Results: Forty-seven out of 2188 documents were included in the analysis, with 13 items identified to assess the quality of positioning, 4 for sharpness, 3 for artefacts, and 2 for exposure parameters. After applying the criteria to BI mammograms, retroglandular fat was not included in 37.3% of the images. The “Pectoral-Nipple-Line” criterion was achieved in 35% of MLO/ML images. The placement of the implant (subpectoral/subglandular) or performing the Eklund had significant influence on the visible anatomy (p = < 0.005), alongside whether the breast was aligned to the detector’s centre. Conclusions: Some of the criteria used to assess standard mammograms were not applicable to BI due to implant overlap. The alignment of the image with the detector’s centre seems to have an impact on the amount of visible tissue. Further studies are necessary to define the appropriate protocol, technique, and suitable quality criteria to assess BI mammograms.publishersversionpublishe

Mammography Services Quality Assurance: Baseline Standards for Latin America and the Caribbean

Fil: Barr, Helen. No especifíca;Fil: Blanco, Susana Alicia Ana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Salud. Instituto Nacional del Cáncer; ArgentinaFil: Butler, Priscilla. No especifíca;Fil: da Paz, María Angela. No especifíca;Fil: Fleitas, Ileana. No especifíca;Fil: Craig, George. No especifíca;Fil: Jimenez, Pablo. No especifíca;Fil: Luciani, Silvana. No especifíca;Fil: Manrique, Javier. No especifíca;Fil: Mazal, Jonathan. No especifíca;Fil: Medlen, Kayiba. No especifíca;Fil: MIller, Colie. No especifíca;Fil: Mora, Patricia. No especifíca;Fil: Valdez Moreno, Martha Elena. No especifíca;Fil: Mosodeen, Murrie. No especifíca;Fil: Mysler, Gustavo. No especifíca;Fil: Nuche-Berenguer, Bernardo. No especifíca;Fil: Pastel, Mary. No especifíca;Fil: Pinochet, Miguel. No especifíca;Fil: Sisney, Gale. No especifíca;Fil: Ruiz Trejo, Cesar. No especifíca;Fil: Saraiya, Mona. No especifíca;Fil: Solis, Esteban. No especifíca;Fil: Swann, Phillip. No especifíca

AAPM medical physics practice guideline 10.a.: Scope of practice for clinical medical physics.

The American Association of Physicists in Medicine (AAPM) is a nonprofit professional society whose primary purposes are to advance the science, education, and professional practice of medical physics. The AAPM has more than 8000 members and is the principal organization of medical physicists in the United States. The AAPM will periodically define new practice guidelines for medical physics practice to help advance the science of medical physics and to improve the quality of service to patients throughout the United States. Existing medical physics practice guidelines will be reviewed for the purpose of revision or renewal, as appropriate, on their fifth anniversary or sooner. Each medical physics practice guideline (MPPG) represents a policy statement by the AAPM, has undergone a thorough consensus process in which it has been subjected to extensive review, and requires the approval of the Professional Council. The medical physics practice guidelines recognize that the safe and effective use of diagnostic and therapeutic radiation requires specific training, skills, and techniques as described in each document. As the review of the previous version of AAPM Professional Policy (PP)-17 (Scope of Practice) progressed, the writing group focused on one of the main goals: to have this document accepted by regulatory and accrediting bodies. After much discussion, it was decided that this goal would be better served through a MPPG. To further advance this goal, the text was updated to reflect the rationale and processes by which the activities in the scope of practice were identified and categorized. Lastly, the AAPM Professional Council believes that this document has benefitted from public comment which is part of the MPPG process but not the AAPM Professional Policy approval process. The following terms are used in the AAPM's MPPGs: Must and Must Not: Used to indicate that adherence to the recommendation is considered necessary to conform to this practice guideline. Should and Should Not: Used to indicate a prudent practice to which exceptions may occasionally be made in appropriate circumstances

Breast compression – An exploration of problem solving and decision-making in mammography

Objective: Breast compression decreases radiation dose and reduces potential for motion and geometric unsharpness, yet there is variability in applied compression force within and between some centres. This article explores the problem solving process applied to the application of breast compression force from the mammography practitioners' perspective. Methods: A qualitative analysis was undertaken using an existing full data set of transcribed qualitative data collected in a phenomenological study of mammography practitioner values, behaviours and beliefs. The data emerged from focus groups conducted at six NHS breast screening centres in England (participant n = 41), and semi-structured interviews with mammography educators (n = 6). A researcher followed a thematic content analysis process to extract data related to mammography compression problem solving, developing a series of categories, themes and sub-themes. Emerging themes were then peer-validated by two other researchers, and developed into a model of practice. Results: Seven consecutive stages contributed towards compression force problem solving: assessing the request; first impressions; explanations and consent; handling the breast and positioning; applying compression force; final adjustments; feedback. The model captures information gathering, problem framing, problem solving and decision making which inform an ‘ideal’ compression scenario. Behavioural problem solving, heuristics and intuitive decision making are reflected within this model. Conclusion: The application of compression should no longer be considered as one single task within mammography, but is now recognised as a seven stage problem solving continuum. This continuum model is the first to be applied to mammography, and is adaptable and transferable to other radiography practice settings

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

MRI of Breast Lesions

In Magnetic Resonance Mammography (MRM) high spatial as well as temporal resolution is of utmost importance for differentiating between malignant and benign lesions. Therefore, a so‐called dynamic technique (i.e., the repetitive imaging of the same slices before and in short time intervals after the injection of contrast medium) is essential to detect the differences in initial enhancements between malignant and benign lesions which are reflected by the tumorangiogenetic vascular network of malignant lesions. This unit presents a basic protocol and several alternate protocols for dynamic MRM.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145284/1/cpmia2101.pd

Regulation 61-64 x-rays (title b)

Except as otherwise specifically provided, these regulations apply to all persons who receive, possess, use, transfer, own, or acquire any x-ray producing machine. The provisions of these regulations shall not be interpreted as limiting the intentional exposure of patients to radiation for the purpose of diagnosis, analysis, or therapy by persons licensed to practice one or more of the health professions within the authority granted to them by statute or regulation

Regulation 61-64 x-rays (title B)

Except as otherwise specifically provided, this regulation applies to all persons who receive, possess, use, transfer, own, or acquire any x-ray producing machine. The provisions of this regulation shall not be interpreted as limiting the intentional exposure of patients to radiation for the purpose of diagnosis, analysis, or therapy by persons licensed to practice one (1) or more of the health professions within the authority granted to them by statute or regulation