Abdominal Multi-Organ Segmentation Using Federated Learning

Abstract

Multi-organ segmentation refers to precisely de-lineating and identifying multiple organs or structures within medical images, such as Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI), to outline boundaries and regions for each organ accurately. Medical imaging is crucial to comprehending and diagnosing a wide range of illnesses for which accurate multi-organ image segmentation is often required for successful analysis. Due to the delicate nature of medical data, traditional methods for multi-organ segmentation include centralizing data, which presents serious privacy problems. This centralized training strategy impedes innovation and collaborative efforts in healthcare by raising worries about patient confidentiality, data security, and reg-ulatory compliance. The development of deep learning-based image segmentation algorithms has been hindered by the lack of fully annotated datasets, and this issue is exacerbated in multi-organ segmentation. Federated Learning (FL) addresses privacy concerns in multi-organ segmentation by enabling model training across decentralized institutions without sharing raw data. Our proposed FL-based model for CT scans ensures data privacy while achieving accurate multi-organ segmentation. By leveraging FL techniques, this paper collaboratively trains segmentation models on local datasets held by distinct medical institutions. The expected outcomes encompass achieving high Dice Similarity Coefficient (DSC) metrics and validating the efficacy of the proposed FL approach in attaining precise and accurate segmentation across diverse medical imaging datasets. © 2024 IEEE.