Image quality assessment by overlapping task-specific and task-agnostic
measures: application to prostate multiparametric MR images for cancer
segmentation
Image quality assessment (IQA) in medical imaging can be used to ensure that
downstream clinical tasks can be reliably performed. Quantifying the impact of
an image on the specific target tasks, also named as task amenability, is
needed. A task-specific IQA has recently been proposed to learn an
image-amenability-predicting controller simultaneously with a target task
predictor. This allows for the trained IQA controller to measure the impact an
image has on the target task performance, when this task is performed using the
predictor, e.g. segmentation and classification neural networks in modern
clinical applications. In this work, we propose an extension to this
task-specific IQA approach, by adding a task-agnostic IQA based on
auto-encoding as the target task. Analysing the intersection between
low-quality images, deemed by both the task-specific and task-agnostic IQA, may
help to differentiate the underpinning factors that caused the poor target task
performance. For example, common imaging artefacts may not adversely affect the
target task, which would lead to a low task-agnostic quality and a high
task-specific quality, whilst individual cases considered clinically
challenging, which can not be improved by better imaging equipment or
protocols, is likely to result in a high task-agnostic quality but a low
task-specific quality. We first describe a flexible reward shaping strategy
which allows for the adjustment of weighting between task-agnostic and
task-specific quality scoring. Furthermore, we evaluate the proposed algorithm
using a clinically challenging target task of prostate tumour segmentation on
multiparametric magnetic resonance (mpMR) images, from 850 patients. The
proposed reward shaping strategy, with appropriately weighted task-specific and
task-agnostic qualities, successfully identified samples that need
re-acquisition due to defected imaging process.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) https://www.melba-journal.or