A dual‐module 3D fusion framework for multi‐modal MRI segmentation in anal fistulae

BackgroundRecent advancements in deep learning have greatly impacted medical image segmentation, especially in the segmentation of human organs and tissues. Accurate segmentation is crucial for precise diagnoses and effective treatment planning. However, segmenting anal fistulae in magnetic resonanc...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Medical physics (Lancaster) Ročník 52; číslo 11; s. e70102 - n/a
Hlavní autoři: Chen, Genlang, Zhou, Yang, Zhang, Jiajian, Cai, Ping, Meng, Xianyun
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.11.2025
Témata:
3D fusion
deep learning
Humans
Imaging, Three-Dimensional - methods
Magnetic Resonance Imaging
MRI segmentation
multi‐model
Rectal Fistula - diagnostic imaging
deep learning
multi‐model
MRI segmentation
3D fusion
ISSN:0094-2405, 2473-4209, 2473-4209
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:BackgroundRecent advancements in deep learning have greatly impacted medical image segmentation, especially in the segmentation of human organs and tissues. Accurate segmentation is crucial for precise diagnoses and effective treatment planning. However, segmenting anal fistulae in magnetic resonance imaging (MRI) images presents a significant challenge due to the similarity between lesions and normal tissues, which often results in high false positive rates. PurposeThis study aims to develop a robust segmentation method for anal fistulae in MRI images that improves accuracy by reducing false positives, while also efficiently handling the complexity of multi‐modal and multi‐directional MRI data. MethodsWe propose a novel dual‐module framework. At the input stage, a inspired method is employed to efficiently capture multi‐dimensional information from the MRI data. At the output stage, residual techniques are applied to optimize and enhance the segmentation results, ensuring comprehensive integration of multi‐modal and multi‐directional MRI information. Additionally, an Outlier‐Penalized Dice Loss (OPDL) function is introduced to specifically address the issue of false positives in segmentation. ResultsThe proposed method was evaluated on a dataset of 950 self‐collected multi‐directional MRI scans. Our approach demonstrated superior segmentation performance compared to six state‐of‐the‐art methods, achieving a Dice Score of 0.7324 and an IoU Score of 0.5943, which represents significant improvements over the highest‐performing baseline models. ConclusionsThe dual‐module framework, combined with the selection mechanism and residual techniques, along with the innovative OPDL, offers a significant advancement in the segmentation of anal fistulae in MRI images. This approach not only improves accuracy by effectively distinguishing between lesions and normal tissues but also reduces computational complexity, making it a promising tool for clinical applications.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0094-2405
2473-4209
2473-4209
DOI:10.1002/mp.70102