PRSNet : Part Relation and Selection Network for Bone Age Assessment

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

13 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019
Subtitle of host publication22nd International Conference, Proceedings, Part VI
EditorsDinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
PublisherSpringer, Cham
Pages413-421
Number of pages9
Edition1
ISBN (Electronic)978-3-030-32226-7
ISBN (Print)978-3-030-32225-0
Publication statusPublished - 2019
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11769 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
PlaceChina
CityShenzhen
Period13 - 17 October 2019

Abstract

Bone age is one of the most important indicators for assessing bone’s maturity, which can help to interpret human’s growth development level and potential progress. In the clinical practice, bone age assessment (BAA) of X-ray images requires the joint consideration of the appearance and location information of hand bones. These kinds of information can be effectively captured by the relation of different anatomical parts of hand bone. Recently developed methods differ mostly in how they model the part relation and choose useful parts for BAA. However, these methods neglect the mining of relationship among different parts, which can help to improve the assessment accuracy. In this paper, we propose a novel part relation module, which accurately discovers the underlying concurrency of parts by using multi-scale context information of deep learning feature representation. Furthermore, based on the part relation, we explore a new part selection module, which comprehensively measures the importance of parts and select the top ranking parts for assisting BAA. We jointly train our part relation and selection modules in an end-to-end way, achieving state-of-the-art performance on the public RSNA 2017 Pediatric Bone Age benchmark dataset and outperforming other competitive methods by a significant margin.

Citation Format(s)

PRSNet : Part Relation and Selection Network for Bone Age Assessment. / Ji, Yuanfeng; Chen, Hao; Lin, Dan; Wu, Xiaohua; Lin, Di.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Proceedings, Part VI. ed. / Dinggang Shen; Tianming Liu; Terry M. Peters; Lawrence H. Staib; Caroline Essert; Sean Zhou; Pew-Thian Yap; Ali Khan. 1. ed. Springer, Cham, 2019. p. 413-421 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11769 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review