Periodic Reporting for period 3 - PULSE (Perception Ultrasound by Learning Sonographic Experience)
Reporting period: 2019-11-01 to 2021-04-30
The original ambition in PULSE is to develop new machine-learning approaches to describe ultrasound video content richly using knowledge of scanning protocols, visual cues determined by gaze-tracking and probe motion. To our knowledge this is the first work to attempt to bridge the gap between an ultrasound device and the user by employing a machine-learning solution that embeds clinical expert knowledge (through measuring perception and actions) to add interpretation power. In a very recent publication we have shown how our approach naturally leads to efficient ultrasound models (meaning models that have significantly less parameters than conventional deep learning models with a small percentage loss in average accuracy) which is an important deployabilty advantage as you ideally want solutions to work on low cost standard consumer (small memory) devices rather than high-performance devices. The hope is that taking this approach may provide a major step towards making ultrasound a more accessible technology to the non-expert across the world.
The innovation in PULSE is to apply the latest ideas from machine learning and computer vision to build, from real world training video data, computational models that describe how an expert sonographer performs a diagnostic study of a subject from multiple perceptual cues. Novel machine-learning based computational model designs are being investigated for different tasks (to date - recognising standard planes, gazed-based image and video navigation, describing sonographer actions, describing ultrasound video via text, and summarising and characterising clinical workflow) based on probe and eye motion tracking, audio, image processing, and knowledge of how to interpret real-world clinical images and videos acquired to a standardised protocol. The underlying premise of our research is that by building models that more closely mimic how a human makes decisions from US images, considerably more efficient and powerful assistive interpretation methods can be built than have previously been possible from still US images and videos alone.
The overall objectives of the technical research is:
1. To develop a rich lexicon of sonographer words (vocabularies and languages) to describe US videos, the annotated datasets, and methods and software for accurately and reliably describing real world clinical ultrasound video content.
2. To build methods and software for describing ultrasound video content both for sonographer training and assistive technologies for clinical tasks.
3. To compare automatic description by using combined US video and probe motion information, and video, probe and eye motion information relative to US video alone.
Software demonstrators will be developed and evaluated on real world obstetric US data in collaboration with clinical experts and trainees to demonstrate the new approach and its potential to move routine US scanning services from hospitals
into the community which would have clear economic, healthcare and social benefits across Europe and beyond.
Progress against these objectives is summarised in other sections.
1. We have developed a custom-built dedicated ultrasound-based system for simultaneously acquiring full-scan ultrasound video, gaze tracking data, and probe motion data. The system is based in an a hospital clinic and capturing data on pregnant women coming for screening scans (first, second of third trimester) and the sonographers who perform the scans. The dataset is unique in the world to our knowledge and being used to both study clinical sonography from a data science perspective for the first time as well as enable technical research on building assistive tools for clinical sonography tasks which are informed by sonographer perceptions and actions.
The PULSE custom-built system allows us to capture information about key perceptual cues – eye movement and probe motion - lost to conventional image-based interpretation algorithms which only have the video stream of images to work with.
Using this we can study the visual search strategies employed by expert and novice sonographers. The eye movement datasets are being added to the PULSE database for algorithm development research.
We are also interested in questions such as whether novices and experts follow different visual search strategies, and whether there are different visual search strategies amongst experts.
Knowledge gleaned from these studies is being used to propose new visual search models (defining key structures used in visual search and visual search patterns) that will be embedded in assistive technologies to support sonography navigition and image reading/interpretation.
We are only half way through the project, so the expected results at project end are still a little difficult to predict. However, we expect them to be in the areas of:
1. Clinical sonography data science - greater understanding of clinical sonography workflow and sonographer skills/skills assessment.
2. Assistive technologies for interpreting ultrasound images - New machine-learning based models to assist in ultrasound standard plane image interpretation.
3. Assistive technologies for ultrasound navigation - New machine-learning based models to assist in ultrasound navigation for simple and complex tasks.
4. Video analysis - natural processing language: methodology to allow key information from hard to interpret ultrasound video to be communicated to a non-sonographer via a text-based description.