Intelligent computing applications to assist perceptual training in medical imaging

The research presented in this thesis represents a body of work which addresses issues in medical imaging, primarily as it applies to breast cancer screening and laparoscopic surgery. The concern here is how computer based methods can aid medical practitioners in these tasks. Thus, research is presented which develops both new techniques of analysing radiologists performance data and also new approaches of examining surgeons visual behaviour when they are undertaking laparoscopic training. Initially a new chest X-Ray self-assessment application is described which has been developed to assess and improve radiologists performance in detecting lung cancer. Then, in breast cancer screening, a method of identifying potential poor performance outliers at an early stage in a national self-assessment scheme is demonstrated. Additionally, a method is presented to optimize whether a radiologist, in using this scheme, has correctly localised and identified an abnormality or made an error. One issue in appropriately measuring radiological performance in breast screening is that both the size of clinical monitors used and the difficulty in linking the medical image to the observer s line of sight hinders suitable eye tracking. Consequently, a new method is presented which links these two items. Laparoscopic surgeons have similar issues to radiologists in interpreting a medical display but with the added complications of hand-eye co-ordination. Work is presented which examines whether visual search feedback of surgeons operations can be useful training aids

Performance testing for radiologists interpreting chest radiographs

Performance testing for radiologists interpreting chest radiograph

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

Visual search behaviour during laparoscopic cadaveric procedures

Laparoscopic surgery provides a very complex example of medical image interpretation. The task entails: visually examining a display that portrays the laparoscopic procedure from a varying viewpoint; eye-hand co-ordination; complex 3D interpretation of the 2D display imagery; efficient and safe usage of appropriate surgical tools, as well as other factors. Training in laparoscopic surgery typically entails practice using surgical simulators. Another approach is to use cadavers. Viewing previously recorded laparoscopic operations is also a viable additional approach and to examine this a study was undertaken to determine what differences exist between where surgeons look during actual operations and where they look when simply viewing the same pre-recorded operations. It was hypothesised that there would be differences related to the different experimental conditions; however the relative nature of such differences was unknown. The visual search behaviour of two experienced surgeons was recorded as they performed three types of laparoscopic operations on a cadaver. The operations were also digitally recorded. Subsequently they viewed the recording of their operations, again whilst their eye movements were monitored. Differences were found in various eye movement parameters when the two surgeons performed the operations and where they looked when they simply watched the recordings of the operations. It is argued that this reflects the different perceptual motor skills pertinent to the different situations. The relevance of this for surgical training is explored

Laparoscopic surgical skills training: an investigation of the potential of using surgeons' visual search behaviour as a performance indicator

Laparoscopic surgery is a difficult perceptual-motor task and effective and efficient training in the technique is important. Viewing previously recorded laparoscopic operations is a possible available training technique for surgeons to increase their knowledge of such minimal access surgery (MAS). It is not well known whether this is a useful technique, how effective it is or what effect it has on the surgeon watching the recorded video. As part of an on-going series of studies into laparoscopic surgery, an experiment was conducted to examine whether surgical skill level has an effect on the visual search behaviour of individuals of different surgical experience when they examine such imagery. Medically naive observers, medical students, junior surgeons and experienced surgeons viewed a laparoscopic recording of a recent operation. Initial examination of the recorded eye movement data indicated commonalities between all observers, largely irrespective of surgical experience. This, it is argued, is due to visual search in this situation largely being driven by the dynamic nature of the images. The data were then examined in terms of surgical steps and also in terms of interventions when differences were found related to surgical experience. Consequently, it is argued that monitoring the eye movements of trainee surgeons whilst they watch pre-recorded operations is a potential useful adjunct to existing training regimes

Breast screening: understanding case difficulty and the nature of errors

In the UK all screeners undertake the PERFORMS scheme where they read annual sets of challenging cases. During this assessment, they give each case a confidence rating on whether it should be recalled. If they decide to recall a case, they also indicate the center of any key mammographic features on a display of the relevant mammographic case view. Expert radiological opinion defines what the key abnormalities (targets) are in any case. Data can then be analyzed using ROC and JAFROC approaches, and particularly for the latter, assessing whether a user has correctly located a feature or not is important. Using image pixel information alone it is possible to delineate correct localization of an abnormality from an incorrect location by defining an area of interest. To explore such location information in more detail, data from the last year of the PERFORMS scheme were reanalyzed and the location responses for each of the 675 participants on 120 screening cases examined. Additionally, expert radiological opinions had been garnered for various reasons, including accurately delineating any abnormalities. An algorithmic approach is developed which assesses whether users’ indications should be included as correct abnormality identification or not, based on the feedback location information of all participants’ indicated locations and the relative position of an indicated location to the abnormality. This approach is proposed to be superior to simple pixel distance approaches which measure a fixed distance from the centre of a target to the user’s indicated location. The approach adds to the experimenter’s repertoire of tools when examining user errors and case difficulty in medical imaging research

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