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WEAKLY SUPERVISED ESTIMATION OF SHADOW CONFIDENCE MAPS IN FETAL ULTRASOUND IMAGING

Oleh   Qingjie Mengnz [-]
Kontributor / Dosen Pembimbing : Matthew Sinclair, Veronika Zimmer , Benjamin Hou, Martin Rajchl , Nicolas Toussaint, Ozan Oktay , Jo Schlemper , Alberto Gomez , James Housden, Jacqueline Matthew, Daniel Rueckert, Julia A. Schnabel, and Bernhard Kai
Jenis Koleksi : Jurnal elektronik
Penerbit : Lain-lain
Fakultas :
Subjek :
Kata Kunci : Ultrasound imaging, deep learning,weakly supervised, shadow detection, confidence estimation
Sumber : IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 38, NO. 12, DECEMBER 2019
Staf Input/Edit : Irwan Sofiyan  
File : 1 file
Tanggal Input : 2021-06-11 09:36:04

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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. .