Federative ischemic stroke segmentation as alternative to overcome domain-shift multi-institution challenges

• URL: http://arxiv.org/abs/2508.18296v1
• Date: Fri, 22 Aug 2025 15:27:13 GMT
• Title: Federative ischemic stroke segmentation as alternative to overcome domain-shift multi-institution challenges
• Authors: Edgar Rangel, Fabio Martinez,
• Abstract summary: This work developed a framework for segmenting ischemic stroke lesions in DWI sequences by sharing knowledge from deep center-independent representations.<n>The model demonstrated strong generalization properties, showing uniform performance across different lesion categories and reliable performance in out-of-distribution centers.
• Score: 1.0742675209112622
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Stroke is the second leading cause of death and the third leading cause of disability worldwide. Clinical guidelines establish diffusion resonance imaging (DWI, ADC) as the standard for localizing, characterizing, and measuring infarct volume, enabling treatment support and prognosis. Nonetheless, such lesion analysis is highly variable due to different patient demographics, scanner vendors, and expert annotations. Computational support approaches have been key to helping with the localization and segmentation of lesions. However, these strategies are dedicated solutions that learn patterns from only one institution, lacking the variability to generalize geometrical lesions shape models. Even worse, many clinical centers lack sufficient labeled samples to adjust these dedicated solutions. This work developed a collaborative framework for segmenting ischemic stroke lesions in DWI sequences by sharing knowledge from deep center-independent representations. From 14 emulated healthcare centers with 2031 studies, the FedAvg model achieved a general DSC of $0.71 \pm 0.24$, AVD of $5.29 \pm 22.74$, ALD of $2.16 \pm 3.60$ and LF1 of $0.70 \pm 0.26$ over all centers, outperforming both the centralized and other federated rules. Interestingly, the model demonstrated strong generalization properties, showing uniform performance across different lesion categories and reliable performance in out-of-distribution centers (with DSC of $0.64 \pm 0.29$ and AVD of $4.44 \pm 8.74$ without any additional training).

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