Pancreas Part Segmentation under Federated Learning Paradigm Article Swipe
Ziliang Hong
,
Halil Ertugrul Aktas
,
Andrea M. Bejar
,
Kathérine C. Wu
,
Hongyi Pan
,
Görkem Durak
,
Zheyuan Zhang
,
Sait Kayali
,
Temel Tirkes
,
Federica Proietto Salanitri
,
Concetto Spampinato
,
Michael Goggins
,
Tamas A. Gonda
,
Candice W. Bolan
,
Rajesh N. Keswani
,
Frank H. Miller
,
Michael B. Wallace
,
Ulaş Bağcı
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2509.23562
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2509.23562
We present the first federated learning (FL) approach for pancreas part(head, body and tail) segmentation in MRI, addressing a critical clinical challenge as a significant innovation. Pancreatic diseases exhibit marked regional heterogeneity cancers predominantly occur in the head region while chronic pancreatitis causes tissue loss in the tail, making accurate segmentation of the organ into head, body, and tail regions essential for precise diagnosis and treatment planning. This segmentation task remains exceptionally challenging in MRI due to variable morphology, poor soft-tissue contrast, and anatomical variations across patients. Our novel contribution tackles two fundamental challenges: first, the technical complexity of pancreas part delineation in MRI, and second the data scarcity problem that has hindered prior approaches. We introduce a privacy-preserving FL framework that enables collaborative model training across seven medical institutions without direct data sharing, leveraging a diverse dataset of 711 T1W and 726 T2W MRI scans. Our key innovations include: (1) a systematic evaluation of three state-of-the-art segmentation architectures (U-Net, Attention U-Net,Swin UNETR) paired with two FL algorithms (FedAvg, FedProx), revealing Attention U-Net with FedAvg as optimal for pancreatic heterogeneity, which was never been done before; (2) a novel anatomically-informed loss function prioritizing region-specific texture contrasts in MRI. Comprehensive evaluation demonstrates that our approach achieves clinically viable performance despite training on distributed, heterogeneous datasets.
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- Type
- preprint
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- Landing Page
- http://arxiv.org/abs/2509.23562
- https://arxiv.org/pdf/2509.23562
- OA Status
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- OpenAlex ID
- https://openalex.org/W4415332569
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https://openalex.org/W4415332569Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.2509.23562Digital Object Identifier
- Title
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Pancreas Part Segmentation under Federated Learning ParadigmWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
- Publication date
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2025-09-28Full publication date if available
- Authors
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Ziliang Hong, Halil Ertugrul Aktas, Andrea M. Bejar, Kathérine C. Wu, Hongyi Pan, Görkem Durak, Zheyuan Zhang, Sait Kayali, Temel Tirkes, Federica Proietto Salanitri, Concetto Spampinato, Michael Goggins, Tamas A. Gonda, Candice W. Bolan, Rajesh N. Keswani, Frank H. Miller, Michael B. Wallace, Ulaş BağcıList of authors in order
- Landing page
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https://arxiv.org/abs/2509.23562Publisher landing page
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https://arxiv.org/pdf/2509.23562Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2509.23562Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.challenges: | 93 |
| abstract_inverted_index.challenging | 72 |
| abstract_inverted_index.delineation | 101 |
| abstract_inverted_index.fundamental | 92 |
| abstract_inverted_index.innovation. | 25 |
| abstract_inverted_index.innovations | 148 |
| abstract_inverted_index.morphology, | 78 |
| abstract_inverted_index.performance | 207 |
| abstract_inverted_index.significant | 24 |
| abstract_inverted_index.soft-tissue | 80 |
| abstract_inverted_index.contribution | 89 |
| abstract_inverted_index.demonstrates | 200 |
| abstract_inverted_index.distributed, | 211 |
| abstract_inverted_index.institutions | 129 |
| abstract_inverted_index.pancreatitis | 41 |
| abstract_inverted_index.prioritizing | 192 |
| abstract_inverted_index.segmentation | 14, 50, 68, 157 |
| abstract_inverted_index.Comprehensive | 198 |
| abstract_inverted_index.architectures | 158 |
| abstract_inverted_index.collaborative | 123 |
| abstract_inverted_index.exceptionally | 71 |
| abstract_inverted_index.heterogeneity | 31 |
| abstract_inverted_index.heterogeneous | 212 |
| abstract_inverted_index.predominantly | 33 |
| abstract_inverted_index.heterogeneity, | 179 |
| abstract_inverted_index.region-specific | 193 |
| abstract_inverted_index.state-of-the-art | 156 |
| abstract_inverted_index.privacy-preserving | 118 |
| abstract_inverted_index.anatomically-informed | 189 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 18 |
| citation_normalized_percentile |