TY - GEN
T1 - Identifying Quantitative and Explanatory Tumor Indexes from Dynamic Contrast Enhanced Ultrasound
AU - Wan, Peng
AU - Liu, Chunrui
AU - Chen, Fang
AU - Qin, Jing
AU - Zhang, Daoqiang
N1 - Funding Information:
Acknowledgement. This work was supported by the National Natural Science Foundation of China (Nos. 62136004, 61876082, 61732006, 61901214, U20A20389), the General Research Fund from Hong Kong Research Grants Council (Nos. 15205919), the National Key R&D Program of China (Grant Nos. 2018YFC2001600, 2018YF C2001602), the Nanjing Medical Science and Technique Development Foundation (Nos. YKK19054), and also by the CAAI-Huawei MindSpore Open Fund.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021/9
Y1 - 2021/9
N2 - Contrast-enhanced ultrasound (CEUS) has been one of the most promising imaging techniques in tumor differential diagnosis since the real-time view of intra-tumor blood microcirculation. Existing studies primarily focus on extracting those discriminative imaging features whereas lack medical explanations. However, accurate quantitation of some clinical experience-driven indexes regarding intra-tumor vascularity, such as tumor infiltration and heterogeneity, still faces significant limitations. To tackle this problem, we present a novel scheme to identify quantitative and explanatory tumor indexes from dynamic CEUS sequences. Specifically, our method mainly comprises three steps: 1) extracting the stable pixel-level perfusion pattern from dynamic CEUS imaging using an improved stable principal component pursuit (SPCP) algorithm; 2) performing local perfusion variation comparison by the proposed Phase-constrained Wasserstein (PCW) distance; 3) estimating three clinical knowledge-induced tumor indexes, i.e. infiltration, regularity, and heterogeneity. The effectiveness of this method was evaluated on our collected CEUS dataset of thyroid nodules, and the resulting infiltration and heterogeneity index with p< 0.05 between different pathological types validated the efficacy of this quantitation scheme in thyroid nodule diagnosis.
AB - Contrast-enhanced ultrasound (CEUS) has been one of the most promising imaging techniques in tumor differential diagnosis since the real-time view of intra-tumor blood microcirculation. Existing studies primarily focus on extracting those discriminative imaging features whereas lack medical explanations. However, accurate quantitation of some clinical experience-driven indexes regarding intra-tumor vascularity, such as tumor infiltration and heterogeneity, still faces significant limitations. To tackle this problem, we present a novel scheme to identify quantitative and explanatory tumor indexes from dynamic CEUS sequences. Specifically, our method mainly comprises three steps: 1) extracting the stable pixel-level perfusion pattern from dynamic CEUS imaging using an improved stable principal component pursuit (SPCP) algorithm; 2) performing local perfusion variation comparison by the proposed Phase-constrained Wasserstein (PCW) distance; 3) estimating three clinical knowledge-induced tumor indexes, i.e. infiltration, regularity, and heterogeneity. The effectiveness of this method was evaluated on our collected CEUS dataset of thyroid nodules, and the resulting infiltration and heterogeneity index with p< 0.05 between different pathological types validated the efficacy of this quantitation scheme in thyroid nodule diagnosis.
KW - Contrast enhanced ultrasound
KW - Perfusion analysis
KW - Quantitative parameters estimation
UR - http://www.scopus.com/inward/record.url?scp=85116455929&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87237-3_61
DO - 10.1007/978-3-030-87237-3_61
M3 - Conference article published in proceeding or book
AN - SCOPUS:85116455929
SN - 9783030872366
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 638
EP - 647
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
A2 - de Bruijne, Marleen
A2 - Cattin, Philippe C.
A2 - Cotin, Stéphane
A2 - Padoy, Nicolas
A2 - Speidel, Stefanie
A2 - Zheng, Yefeng
A2 - Essert, Caroline
PB - Springer Science and Business Media Deutschland GmbH
T2 - 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Y2 - 27 September 2021 through 1 October 2021
ER -