Segmentation of hip joint anatomy structures from radiographic images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0201507" target="_blank" >RIV/00216305:26220/26:0201507 - isvavai.cz</a>

Výsledek na webu

<a href="https://hdl.handle.net/11012/255319" target="_blank" >https://hdl.handle.net/11012/255319</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13164/eeict.2025.68" target="_blank" >10.13164/eeict.2025.68</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Segmentation of hip joint anatomy structures from radiographic images

Popis výsledku v původním jazyce

This paper deals with the problem of a hip joint segmentation in radiographic images with the use of a deep learning approach. The paper is focused on training nnU-Net models and creating an original dataset that contains 150 radiographs, 100 training and 50 test images. There are six trained models, five from cross-validation training and one trained on all training data. All models are evaluated on the test dataset using the Dice score for individual labels and the combined mean Dice score for the image. The best-performing model was the model trained on all training images. The most challenging labels for segmentation were those representing the Kohler teardrop and ¨ the space between the femoral head, teardrop and acetabulum due to their size and variability observed across the dataset.

Název v anglickém jazyce

Segmentation of hip joint anatomy structures from radiographic images

Popis výsledku anglicky

This paper deals with the problem of a hip joint segmentation in radiographic images with the use of a deep learning approach. The paper is focused on training nnU-Net models and creating an original dataset that contains 150 radiographs, 100 training and 50 test images. There are six trained models, five from cross-validation training and one trained on all training data. All models are evaluated on the test dataset using the Dice score for individual labels and the combined mean Dice score for the image. The best-performing model was the model trained on all training images. The most challenging labels for segmentation were those representing the Kohler teardrop and ¨ the space between the femoral head, teardrop and acetabulum due to their size and variability observed across the dataset.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings II of the 31st Conference STUDENT EEICT 2025: Selected papers.

ISBN

978-80-214-6320-2

ISSN

—

e-ISSN

2788-1334

Počet stran výsledku

4

Strana od-do

68-71

Název nakladatele

Brno University of Technology

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

29. 4. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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