Captioning Ultrasound Images Automatically.

Deep Learning Fetal Ultrasound Image Captioning Image Description Natural Language Processing Recurrent Neural Networks

Journal

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Titre abrégé: Med Image Comput Comput Assist Interv
Pays: Germany
ID NLM: 101249582

Informations de publication

Date de publication:
Oct 2019
Historique:
entrez: 25 1 2020
pubmed: 25 1 2020
medline: 25 1 2020
Statut: ppublish

Résumé

We describe an automatic natural language processing (NLP)-based image captioning method to describe fetal ultrasound video content by modelling the vocabulary commonly used by sonographers and sonologists. The generated captions are similar to the words spoken by a sonographer when describing the scan experience in terms of visual content and performed scanning actions. Using full-length second-trimester fetal ultrasound videos and text derived from accompanying expert voice-over audio recordings, we train deep learning models consisting of convolutional neural networks and recurrent neural networks in merged configurations to generate captions for ultrasound video frames. We evaluate different model architectures using established general metrics (

Identifiants

pubmed: 31976493
doi: 10.1007/978-3-030-32251-9_37
pmc: PMC6978141
mid: EMS85514
doi:

Types de publication

Journal Article

Langues

eng

Pagination

338-346

Subventions

Organisme : European Research Council
ID : 694581
Pays : International

Références

Behav Res Methods. 2010 May;42(2):381-92
pubmed: 20479170
Neural Comput. 1997 Nov 15;9(8):1735-80
pubmed: 9377276

Auteurs

Mohammad Alsharid (M)

University of Oxford, Oxford, UK.

Harshita Sharma (H)

University of Oxford, Oxford, UK.

Lior Drukker (L)

University of Oxford, Oxford, UK.

Pierre Chatelain (P)

University of Oxford, Oxford, UK.

Aris T Papageorghiou (AT)

University of Oxford, Oxford, UK.

J Alison Noble (JA)

University of Oxford, Oxford, UK.

Classifications MeSH