Automated Assessment of Bone Age Using Deep Learning and Gaussian Process Regression
Bone age is an essential measure of skeletal maturity in children with growth disorders. It is typically assessed by a trained physician using radiographs of the hand and a reference model. However, it has been described that the reference models leave room for interpretation leading to a large inter-observer and intra-observer variation. In this work, we explore a novel method for automated bone age assessment to assist physicians with their estimation. It consists of a powerful combination of deep learning and Gaussian process regression. Using this combination, sensitivity of the deep learning model to rotations and flips of the input images can be exploited to increase overall predictive performance compared to only using the deep learning network. We validate our approach retrospectively on a set of 12611 radiographs of patients between 0 and 19 years of age.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2018 |
|---|---|
| Erschienen: |
2018 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2018 |
|---|---|
| Enthalten in: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference - 2018(2018) vom: 11. Juli, Seite 674-677 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Van Steenkiste, Tom [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 25.09.2019 Date Revised 28.09.2020 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.1109/EMBC.2018.8512334 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM290726603 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM290726603 | ||
| 003 | DE-627 | ||
| 005 | 20231225065337.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2018 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1109/EMBC.2018.8512334 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n0969.xml |
| 035 | |a (DE-627)NLM290726603 | ||
| 035 | |a (NLM)30440486 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Van Steenkiste, Tom |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Automated Assessment of Bone Age Using Deep Learning and Gaussian Process Regression |
| 264 | 1 | |c 2018 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 25.09.2019 | ||
| 500 | |a Date Revised 28.09.2020 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Bone age is an essential measure of skeletal maturity in children with growth disorders. It is typically assessed by a trained physician using radiographs of the hand and a reference model. However, it has been described that the reference models leave room for interpretation leading to a large inter-observer and intra-observer variation. In this work, we explore a novel method for automated bone age assessment to assist physicians with their estimation. It consists of a powerful combination of deep learning and Gaussian process regression. Using this combination, sensitivity of the deep learning model to rotations and flips of the input images can be exploited to increase overall predictive performance compared to only using the deep learning network. We validate our approach retrospectively on a set of 12611 radiographs of patients between 0 and 19 years of age | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Ruyssinck, Joeri |e verfasserin |4 aut | |
| 700 | 1 | |a Janssens, Olivier |e verfasserin |4 aut | |
| 700 | 1 | |a Vandersmissen, Baptist |e verfasserin |4 aut | |
| 700 | 1 | |a Vandecasteele, Florian |e verfasserin |4 aut | |
| 700 | 1 | |a Devolder, Pieter |e verfasserin |4 aut | |
| 700 | 1 | |a Achten, Eric |e verfasserin |4 aut | |
| 700 | 1 | |a Van Hoecke, Sofie |e verfasserin |4 aut | |
| 700 | 1 | |a Deschrijver, Dirk |e verfasserin |4 aut | |
| 700 | 1 | |a Dhaene, Tom |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference |d 2007 |g 2018(2018) vom: 11. Juli, Seite 674-677 |w (DE-627)NLM175007292 |x 2694-0604 |7 nnns |
| 773 | 1 | 8 | |g volume:2018 |g year:2018 |g day:11 |g month:07 |g pages:674-677 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1109/EMBC.2018.8512334 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 2018 |j 2018 |b 11 |c 07 |h 674-677 | ||