Pancreas Part Segmentation under Federated Learning Paradigm

• URL: http://arxiv.org/abs/2509.23562v1
• Date: Sun, 28 Sep 2025 01:42:43 GMT
• Title: Pancreas Part Segmentation under Federated Learning Paradigm
• Authors: Ziliang Hong, Halil Ertugrul Aktas, Andrea Mia Bejar, Katherine Wu, Hongyi Pan, Gorkem Durak, Zheyuan Zhang, Sait Kayali, Temel Tirkes, Federica Proietto Salanitri, Concetto Spampinato, Michael Goggins, Tamas Gonda, Candice Bolan, Raj Keswani, Frank Miller, Michael Wallace, Ulas Bagci,
• Abstract summary: We present the first federated learning (FL) approach for pancreas part(head, body and tail) segmentation in MRI.<n>This 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.<n>We introduce a privacy-preserving FL framework that enables collaborative model training across seven medical institutions without direct data sharing.
• Score: 18.670706296080084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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