Behnaz Pirzamanbin
Biträdande universitetslektor
Deep Learning for Resolving 3D Microstructural Changes in the Fibrotic Liver
Författare
Redaktör
- Shandong Wu
- Behrouz Shabestari
- Lei Xing
Summary, in English
Portal hypertension, a life-threatening complication of cirrhosis,
is largely triggered by increased intrahepatic vascular resistance.
Fibrosis, regenerative nodule formation, intrahepatic angiogenisis and sinusoidal
remodelling are classical mechanisms that account for increased
intrahepatic vascular resistance in cirrhosis. Our study leverages highresolution
3D synchrotron radiation-based microtomography and a deep
learning-based segmentation approach to investigate these microstructural
changes in the liver. By employing a multi-planar U-Net model,
trained using annotated tomographic slices sourced from our developed
online learning tool, we effectively quantify critical vascular parameters
such as sinusoid proportions, local thickness, and connectivity. These
insights advance our understanding of liver microarchitecture and also
allows correlating vascular parameters to inflammation and fibrosis severity.
Understanding and quantifying these microstructural changes is essential
to be able to predict the transition from seemingly benign conditions
like steatosis or mild inflammation to severe fibrosis and cirrhosis
is largely triggered by increased intrahepatic vascular resistance.
Fibrosis, regenerative nodule formation, intrahepatic angiogenisis and sinusoidal
remodelling are classical mechanisms that account for increased
intrahepatic vascular resistance in cirrhosis. Our study leverages highresolution
3D synchrotron radiation-based microtomography and a deep
learning-based segmentation approach to investigate these microstructural
changes in the liver. By employing a multi-planar U-Net model,
trained using annotated tomographic slices sourced from our developed
online learning tool, we effectively quantify critical vascular parameters
such as sinusoid proportions, local thickness, and connectivity. These
insights advance our understanding of liver microarchitecture and also
allows correlating vascular parameters to inflammation and fibrosis severity.
Understanding and quantifying these microstructural changes is essential
to be able to predict the transition from seemingly benign conditions
like steatosis or mild inflammation to severe fibrosis and cirrhosis
Avdelning/ar
- eSSENCE: The e-Science Collaboration
- Statistiska institutionen
- Muskelbiologi
- EXODIAB: Excellence of Diabetes Research in Sweden
- Autoimmunity
- Diabetiska komplikationer
Publiceringsår
2025-02-08
Språk
Engelska
Sidor
74-84
Publikation/Tidskrift/Serie
Lecture Notes in Computer Science
Volym
15384
Dokumenttyp
Konferensbidrag
Förlag
Springer
Ämne
- Other Mathematics
Conference name
International Conference on Medical Image Computing and Computer-Assisted Intervention - Applications of Medical Artificial Intelligence
Conference date
2024-10-06 - 2024-10-10
Conference place
Marrakesh, Morocco
Aktiv
Published
Forskningsgrupp
- Muscle Biology
- Autoimmunity
- Diabetic Complications
ISBN/ISSN/Övrigt
- ISSN: 1611-3349
- ISBN: 978-3-031-82007-6
- ISBN: 978-3-031-82006-9