Using eye gaze in intelligent interactive imaging training

Medical imaging, particularly in breast cancer screening, requires very skilled interpretation only carried out by specially trained radiologists. A key issue is how to train such skilled behaviour? Recent changes to breast imaging has seen the introduction of high resolution digital imaging which facilitates intelligent interactive training. It has also enabled potential computer aided detection of abnormalities. However, this also tends to increase false positive cancer detections. A series of experiments are reported which examine the role of eye gaze and expertise in inspecting these images. It is proposed that current training approaches could be augmented by including aspects of the eye gaze behaviour of expert screening radiologists together with computer aided detection in new practical interactive training systems

Evaluation of a computer-aided detection (CAD)-enhanced 2D synthetic mammogram: comparison with standard synthetic 2D mammograms and conventional 2D digital mammography

Digital breast tomosynthesis (DBT) when combined with standard 2D digital mammography has been shown to improve the performance of breast cancer screening by increasing cancer detection rates [1-5]. The 2D component remains an important part of the examination and is used to facilitate assessment of symmetry between the breasts, aid comparison with prior mammograms and identify the presence of breast microcalcifications where the evidence for detection with DBT is less robust [1]. The mean glandular dose per view of a DBT image is around 2.3 mGy, which is between 1 - 1.5x more than the dose of standard 2D digital mammography [6]. Acquiring both a DBT and standard 2D digital mammogram on each woman leads to at least a doubling of the radiation dose, which may not be considered acceptable in an asymptomatic screening population. Consequently there has been much interest in the generation of synthetic 2D mammograms from the DBT data set eliminating the additional radiation burden of a separate 2D digital mammogram. There is evidence from prospective and retrospective studies to support the use of synthetic 2D mammograms [5,7-9]. Several retrospective multi-reader studies, including the UK TOMMY trial, have demonstrated comparable performance between synthetic and conventional 2D mammography [7,8]. The Oslo and Storm-2 prospective studies of DBT in breast cancer screening found equivalent cancer detection rates regardless of whether the conventional 2D or the synthetic mammograms were read, concluding that synthetic mammograms were an acceptable replacement for directly acquired conventional 2D mammograms [5,9]. Another approach to improve performance is to combine the synthesised image with a Computer Aided Detection (CAD) algorithm. CAD has been used over the years to assist with the interpretation of 2D mammography. CAD software places marks or prompts on the images to draw the reader’s attention to potential areas of concern, reducing observational oversights. A CAD algorithm has been developed with machine learning technology (iCAD Inc., Nashua , NH, USA and GE Healthcare, Buc, France) to assist in the detection of breast cancer on DBT images. Unlike a conventional CAD system which places marks on the image, areas of concern are automatically identified on each tomosynthesis slice and then blended onto a 2D synthetic image to provide a single CAD enhanced 2D synthetic image for each mammographic projection. The aim of this study was to evaluate the diagnostic performance of the CAD enhanced synthetic mammogram in comparison with standard 2D synthetic mammograms generated from the DBT data set and standard 2D digital mammography

Performance differences across the Atlantic when UK and USA radiologists read the same set of test screening cases

Two groups of experienced radiologists from the UK and the USA read the same set of 40 recent FFDM screening cases to examine the effects of mammography experience, volume of cases read per year, screening practice and monitor resolution on performance,. Sixteen American radiologists reported these cases using twin DICOM calibrated monitors which were half the resolution of the clinical mammographic workstations used by 16 UK radiologists. In terms of effects of volume of cases read per year, then when the group of American radiologists were split into high and low volume readers (using 5,000 cases p.a. as a criterion) no difference in any performance measure was found. This may be partly explained by the fact that they were all were very experienced which may have counteracted any case volume effect here. Comparing the two groups of radiologists from both countries, then the UK group performed better in terms of the number of cancers detected although the American group recalled more cases, despite having poorer monitors. This reflects differences in clinical screening practice between the countries, however differences simply due to the reporting monitors used cannot be ruled out. Data from the study were also compared to that from all UK screeners who had read these cases as either soft copy or as mammographic film

The implementation of an AR (augmented reality) approach to support mammographic interpretation training - an initial feasibility study

Appropriate feedback plays an important role in optimising mammographic interpretation training whilst also ensuring good interpretation performance. The traditional keyboard, mouse and workstation technical approach has a critical limitation in providing supplementary image-related information and providing complex feedback in real time. Augmented Reality (AR) provides a possible superior approach in this situation, as feedback can be provided directly overlaying the displayed mammographic images so making a generic approach which can also be vendor neutral. In this study, radiological feedback was dynamically remapped virtually into the real world, using perspective transformation, in order to provide a richer user experience in mammographic interpretation training. This is an initial attempt of an AR approach to dynamically superimpose pre-defined feedback information of a DICOM image on top of a radiologist’s view, whilst the radiologist is examining images on a clinical workstation. The study demonstrates the feasibility of the approach, although there are limitations on interactive operations which are due to the hardware used. The results of this fully functional approach provide appropriate feedback/image correspondence in a simulated mammographic interpretation environment. Thus, it is argued that employing AR is a feasible way to provide rich feedback in the delivery of mammographic interpretation training

Health professionals’ agreement on density judgements and successful abnormality identification within the UK Breast Screening Programme

Higher breast density is associated with a greater chance of developing breast cancer. Additionally, it is well known that higher mammographic breast density is associated with increased difficulty in accurately identifying breast cancer. However, comparatively little is known of the reliability of breast density judgements. All UK breast screeners (primarily radiologists and technologists) annually participate in the PERFORMS self-assessment scheme where they make several judgements about series of challenging recent screening cases of known outcomes. As part of this process, for each case, they provide a radiological assessment of the likelihood of cancer on a confidence scale, alongside an assessment of case density using a three point scale. Analysis of the data from two years of the scheme found that the degree of agreement on case density was significantly greater than no agreement (p < .001). However, only a moderate degree of inter-rater reliability was exhibited (κ = .44) with significant differences between the occupational groups. The reasons for differences between the occupational groups and the relationship between agreement on density rating and case reading ability are explored

Mammographic interpretation training profile in the UK: current difficulties and future outlook

In the UK, most mammographic interpretation training needs to be undertaken where there is a mammo-alternator or other suitable light box; consequently limiting the time and places where training can take place. However, the gradual introduction of digital mammography is opening up new opportunities of providing such training without the restriction of current viewing devices. Whilst high-resolution monitors in appropriate viewing environments are de rigour for actual reporting; advantages of the digital image over film are in the flexibility of training opportunity afforded, e.g. training whenever, wherever suits the individual. A previous study indicated the possible potential for reporting mammographic cases utilising handheld devices with suitable interaction techniques. In a pilot study, a group of mammographers (n=4) were questioned in semi-structured interviews in order to help establish current UK film-readers’ training profile. On the basis of the pilot study data, 109 Breast Screening Units (601 film readers) were approached to complete a structured questionnaire in order to establish the potential role of smaller computer devices in mammographic interpretation training (given the use of digital mammography). Subsequently, a study of radiologists' visual search behaviour in digital screening has begun. This has highlighted different image manipulations than found in structured experiments in this area and poses new challenges for visualising the inspection process. Overall the results indicate that using different display sizes for training is possible but is also a challenging task requiring novel interaction approaches

Performance testing for radiologists interpreting chest radiographs

Performance testing for radiologists interpreting chest radiograph

How new readers perform as compared to more experienced readers on the PERFORMS scheme

How new readers perform as compared to more experienced readers on the PERFORMS schem

A potential method to identify poor breast screening performance

In the UK all breast screeners undertake the PERFORMS scheme where they annually read case sets of challenging cases. From the subsequent data it is possible to identify any individual who is performing significantly lower than their peers. This can then facilitate them being offered further targeted training to improve performance. However, currently this under-performance can only be calculated once all screeners have taken part, which means the feedback can potentially take several months. To determine whether such performance outliers could usefully be identified approximately much earlier the data from the last round of the scheme were re-analysed. From the information of 283 participants, 1,000 groups of them were selected randomly for fixed group sizes varying from four to 50 individuals. After applying bootstrapping on 1,000 groups, a distribution of low performance threshold values was constructed. Then the accuracy of estimation was determined by calculating the median value and standard error of this distribution as compared with the known actual results. Data indicate that increasing sample sizes improved the estimation of the median and decreased the standard error. Using information from as few as 25 individuals allowed an approximation of the known outlier cut off value and this improved with larger sample sizes. This approach is now implemented in the PERFORMS scheme to enable individuals who have difficulties, as compared to their peers, to be identified very early after taking part which can then help them to improve their performance

How quickly do breast screeners learn their skills?

The UK’s Breast Screening Programme is 27 years old and many experienced breast radiologists are now retiring, coupled with an influx of new screening personnel. It is important to the ongoing Programme that new mammography readers are quickly up to the skill level of experienced readers. This raises the question of how quickly the necessary cancer detection skills are learnt. All breast screening radiologists in the UK read educational training sets of challenging FFDM images (the PERFORMS® scheme) yearly to maintain and improve their performance in real life screening. Data were examined from the PERFORMS® annual scheme for 54 new screeners, 55 screeners who have been screening for one year and also for more experienced screeners (597 screeners). Not surprisingly, significant differences in cancer detection rate were found between new readers and both of the other groups. Additionally, the performance of 48 new readers who have now been screening for about a year and have taken part twice in the PERFORMS® scheme were further examined where again a significant difference in cancer detection was found. These data imply that cancer detection skills are learnt quickly in the first year of screening. Information was also examined concerning the volume of cases participants read and other factors