Szczegóły publikacji

Opis bibliograficzny

Multi-step segmentation of pelvic fractures: handling variable fracture counts through anatomical and surface analysis / Artur JURGAS, Maciej Stanuch, Marek WODZIŃSKI, Andrzej SKALSKI // W: Skin image analysis, and computer-aided pelvic imaging for female health : 10th international workshop, ISIC 2025 and first international workshop, CAPI 2025 held in conjunction with MICCAI 2025 : Daejeon, South Korea, September 23, 2025 : proceedings / eds. M. Emre Celebi, [et al.]. — Cham : Springer Nature Switzerland, cop. 2026. — ( Lecture Notes in Computer Science ; ISSN  0302-9743 ; 16149 ). — ISBN: 978-3-032-05824-9; e-ISBN: 978-3-032-05825-6. — S. 103–112. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-09-24. — A. Jurgas, A. Skalski – dod. afiliacja: MedApp S.A, Krakow, Poland

Autorzy (4)

Słowa kluczowe

deep learningpelvic fracturesCT imagingfemale healthsegmentation

Dane bibliometryczne

ID BaDAP163136
Data dodania do BaDAP2025-10-02
DOI10.1007/978-3-032-05825-6_10
Rok publikacji2026
Typ publikacjimateriały konferencyjne (aut.)
Otwarty dostęptak
WydawcaSpringer
KonferencjaMedical Image Computing and Computer-Assisted Intervention 2025
Czasopismo/seriaLecture Notes in Computer Science

Abstract

Pelvic fractures pose a clinical challenge in women due to the complexity of the pelvic structure and adjacent organs. This paper introduces a step-by-step deep learning pipeline for segmenting pelvic fractures in CT imaging, specifically for female health. Using anatomical and fracture surface segmentation, our method addresses fracture pattern variability. We highlight the clinical impact on women’s physical, sexual, and reproductive health and describe our technical methods. Our results show the potential for computer-aided imaging to enhance diagnostic accuracy and segmentation for women’s pelvic trauma, especially in urgent clinical situations. Our code, models, and 54 newly annotated cases are available at https://github.com/Jarartur/multi-step-pelvic-fractures.

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