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Leveraging Shape and Spatial Information for Spontaneous Preterm Birth Prediction. / Pegios, Paraskevas; Sejer, Emilie Pi Fogtmann; Lin, Manxi; Bashir, Zahra; Svendsen, Morten Bo Søndergaard; Nielsen, Mads; Petersen, Eike; Christensen, Anders Nymark; Tolsgaard, Martin; Feragen, Aasa.
Simplifying Medical Ultrasound - 4th International Workshop, ASMUS 2023, Held in Conjunction with MICCAI 2023, Proceedings. ed. / Bernhard Kainz; Johanna Paula Müller; Bernhard Kainz; Alison Noble; Julia Schnabel; Bishesh Khanal; Thomas Day. Springer, 2023. p. 57-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14337 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Pegios, P, Sejer, EPF, Lin, M, Bashir, Z, Svendsen, MBS
, Nielsen, M, Petersen, E, Christensen, AN
, Tolsgaard, M & Feragen, A 2023,
Leveraging Shape and Spatial Information for Spontaneous Preterm Birth Prediction. in B Kainz, JP Müller, B Kainz, A Noble, J Schnabel, B Khanal & T Day (eds),
Simplifying Medical Ultrasound - 4th International Workshop, ASMUS 2023, Held in Conjunction with MICCAI 2023, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14337 LNCS, pp. 57-67, 4th International Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2023, Vancouver, Canada,
08/10/2023.
https://doi.org/10.1007/978-3-031-44521-7_6APA
Pegios, P., Sejer, E. P. F., Lin, M., Bashir, Z., Svendsen, M. B. S.
, Nielsen, M., Petersen, E., Christensen, A. N.
, Tolsgaard, M., & Feragen, A. (2023).
Leveraging Shape and Spatial Information for Spontaneous Preterm Birth Prediction. In B. Kainz, J. P. Müller, B. Kainz, A. Noble, J. Schnabel, B. Khanal, & T. Day (Eds.),
Simplifying Medical Ultrasound - 4th International Workshop, ASMUS 2023, Held in Conjunction with MICCAI 2023, Proceedings (pp. 57-67). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14337 LNCS
https://doi.org/10.1007/978-3-031-44521-7_6Vancouver
Pegios P, Sejer EPF, Lin M, Bashir Z, Svendsen MBS
, Nielsen M et al.
Leveraging Shape and Spatial Information for Spontaneous Preterm Birth Prediction. In Kainz B, Müller JP, Kainz B, Noble A, Schnabel J, Khanal B, Day T, editors, Simplifying Medical Ultrasound - 4th International Workshop, ASMUS 2023, Held in Conjunction with MICCAI 2023, Proceedings. Springer. 2023. p. 57-67. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14337 LNCS).
https://doi.org/10.1007/978-3-031-44521-7_6Author
Pegios, Paraskevas ; Sejer, Emilie Pi Fogtmann ; Lin, Manxi ; Bashir, Zahra ; Svendsen, Morten Bo Søndergaard ; Nielsen, Mads ; Petersen, Eike ; Christensen, Anders Nymark ; Tolsgaard, Martin ; Feragen, Aasa. / Leveraging Shape and Spatial Information for Spontaneous Preterm Birth Prediction. Simplifying Medical Ultrasound - 4th International Workshop, ASMUS 2023, Held in Conjunction with MICCAI 2023, Proceedings. editor / Bernhard Kainz ; Johanna Paula Müller ; Bernhard Kainz ; Alison Noble ; Julia Schnabel ; Bishesh Khanal ; Thomas Day. Springer, 2023. pp. 57-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14337 LNCS).
Bibtex
@inproceedings{6ccef1ca5ea6455297e78038f7e90115,
title = "Leveraging Shape and Spatial Information for Spontaneous Preterm Birth Prediction",
abstract = "Spontaneous preterm birth prediction from transvaginal ultrasound images is a challenging task of profound interest in gynecological obstetrics. Existing works are often validated on small datasets and may lack validation of model calibration and interpretation. In this paper, we present a comprehensive study of methods for predicting preterm birth from transvaginal ultrasound using a large clinical dataset. We propose a shape- and spatially-aware network that leverages segmentation predictions and pixel spacing information as additional input to enhance predictions. Our model demonstrates competitive performance on our benchmark, providing additional interpretation and achieving the highest performance across both clinical and machine learning baselines. Through our evaluation, we provide additional insights which we hope may lead to more accurate predictions of preterm births going forwards.",
keywords = "Spontaneous Preterm Birth, Transparency, Transvaginal Ultrasound",
author = "Paraskevas Pegios and Sejer, {Emilie Pi Fogtmann} and Manxi Lin and Zahra Bashir and Svendsen, {Morten Bo S{\o}ndergaard} and Mads Nielsen and Eike Petersen and Christensen, {Anders Nymark} and Martin Tolsgaard and Aasa Feragen",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 4th International Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2023 ; Conference date: 08-10-2023 Through 08-10-2023",
year = "2023",
doi = "10.1007/978-3-031-44521-7_6",
language = "English",
isbn = "9783031445200",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "57--67",
editor = "Bernhard Kainz and M{\"u}ller, {Johanna Paula} and Bernhard Kainz and Alison Noble and Julia Schnabel and Bishesh Khanal and Thomas Day",
booktitle = "Simplifying Medical Ultrasound - 4th International Workshop, ASMUS 2023, Held in Conjunction with MICCAI 2023, Proceedings",
address = "Switzerland",
}
RIS
TY - GEN
T1 - Leveraging Shape and Spatial Information for Spontaneous Preterm Birth Prediction
AU - Pegios, Paraskevas
AU - Sejer, Emilie Pi Fogtmann
AU - Lin, Manxi
AU - Bashir, Zahra
AU - Svendsen, Morten Bo Søndergaard
AU - Nielsen, Mads
AU - Petersen, Eike
AU - Christensen, Anders Nymark
AU - Tolsgaard, Martin
AU - Feragen, Aasa
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Spontaneous preterm birth prediction from transvaginal ultrasound images is a challenging task of profound interest in gynecological obstetrics. Existing works are often validated on small datasets and may lack validation of model calibration and interpretation. In this paper, we present a comprehensive study of methods for predicting preterm birth from transvaginal ultrasound using a large clinical dataset. We propose a shape- and spatially-aware network that leverages segmentation predictions and pixel spacing information as additional input to enhance predictions. Our model demonstrates competitive performance on our benchmark, providing additional interpretation and achieving the highest performance across both clinical and machine learning baselines. Through our evaluation, we provide additional insights which we hope may lead to more accurate predictions of preterm births going forwards.
AB - Spontaneous preterm birth prediction from transvaginal ultrasound images is a challenging task of profound interest in gynecological obstetrics. Existing works are often validated on small datasets and may lack validation of model calibration and interpretation. In this paper, we present a comprehensive study of methods for predicting preterm birth from transvaginal ultrasound using a large clinical dataset. We propose a shape- and spatially-aware network that leverages segmentation predictions and pixel spacing information as additional input to enhance predictions. Our model demonstrates competitive performance on our benchmark, providing additional interpretation and achieving the highest performance across both clinical and machine learning baselines. Through our evaluation, we provide additional insights which we hope may lead to more accurate predictions of preterm births going forwards.
KW - Spontaneous Preterm Birth
KW - Transparency
KW - Transvaginal Ultrasound
U2 - 10.1007/978-3-031-44521-7_6
DO - 10.1007/978-3-031-44521-7_6
M3 - Article in proceedings
AN - SCOPUS:85174715088
SN - 9783031445200
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 57
EP - 67
BT - Simplifying Medical Ultrasound - 4th International Workshop, ASMUS 2023, Held in Conjunction with MICCAI 2023, Proceedings
A2 - Kainz, Bernhard
A2 - Müller, Johanna Paula
A2 - Kainz, Bernhard
A2 - Noble, Alison
A2 - Schnabel, Julia
A2 - Khanal, Bishesh
A2 - Day, Thomas
PB - Springer
T2 - 4th International Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2023
Y2 - 8 October 2023 through 8 October 2023
ER -
