The impact of specialty settings on the perceived quality of medical ultrasound video

Health care professionals are increasingly viewing medical images and videos in a variety of environments. The perception of medical visual information across all specialties, career stages, and practice settings are critical to patient care and patient safety. Visual signal distortions, such as various types of noise and artifacts arising in medical imaging, affect the perceptual quality of visual content and potentially impact diagnoses. To optimize clinical practice, it is of fundamental importance to understand the way medical experts perceive visual quality. Psychophysical studies have been undertaken to evaluate the impact of visual distortions on the perceived quality of medical images and videos. However, very little research has been conducted on how speciality settings affect the perception of visual quality. In this paper, we investigate whether and how radiologists and sonographers differently perceive the quality of compressed ultrasound videos, via a dedicated subjective experiment. The findings can be used to develop useful solutions for improved visual experience and better image-based diagnoses

Clinical and histological data of study cohort.

<p>Clinical and histological data of study cohort.</p

Sample distribution of cluster-model healthy controls vs. patients with colorectal carcinoma (CA).

<p>The specificity above 85% point and the maximum Youden index point meet at a point 0.84 (Red line).</p

Sample distribution of cluster-model healthy controls vs. patient with advanced adenoma (AA).

<p>The specificity above 85% point and the maximum Youden index point meet at a point 2 (red line).</p

Workflow from gene screening, through gene selection, to experimental identification of a disease predictor.

<p>Workflow from gene screening, through gene selection, to experimental identification of a disease predictor.</p

ROC analysis and AUC of cluster-model Healthy-CA.

<p>A. Case processing summary specifying valid sample numbers and labels. B. Receiver operating characteristic (ROC) curve analysis for the cluster-model Healthy-CA. C. Test Result Variable (s) of the computed Y ~ BAD+11 x NEK6-48 model all including area under the curve, standard error; asymptotic significance (and asymptotic 95% confidence interval. C.^a. under the nonparametric assumption. C.^b. null hypothesis: true area = 0.5.</p

Numbers of common genes between Affymetrix arrays and gene lists and number of genes used for the different steps of qPCR selections.

<p>Numbers of common genes between Affymetrix arrays and gene lists and number of genes used for the different steps of qPCR selections.</p