2019 JRNL PP Qingjie Meng - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Detecting acoustic shadows in ultrasound
images is important in many clinical and engineering
applications. Real-time feedback of acoustic shadows can
guide sonographers to a standardized diagnostic viewing
plane with minimal artifacts and can provide additional
information for other automatic image analysis algorithms.
However, automatically detecting shadow regions
using learning-based algorithms is challenging because
pixel-wise ground truth annotation of acoustic shadows
is subjective and time consuming. In this paper, we propose
a weakly supervisedmethod for automatic confidence
estimation of acoustic shadow regions. Our method is
able to generate a dense shadow-focused confidence map.
In our method, a shadow-seg module is built to learn general
shadow features for shadow segmentation, based on
global image-level annotations as well as a small number of
coarse pixel-wise shadow annotations. A transfer function
is introducedto extend the obtainedbinary shadowsegmentation
to a reference confidence map. In addition, a confidence
estimation network is proposed to learn the mapping
between input images and the reference confidence maps.
This network is able to predict shadow confidence maps
directly from input images during inference. We use evaluation
metrics such as DICE, inter-class correlation, and so
on, to verify the effectiveness of our method. Our method
is more consistent than human annotation and outperforms
the state-of-the-art quantitatively in shadow segmen-
tation and qualitatively in confidence estimation of shadow
regions. Furthermore, we demonstrate the applicability of
our method by integrating shadow confidence maps into
tasks such as ultrasound image classification, multi-view
image fusion, and automated biometric measurements.
.