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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Metadata Conditioning via Feature-Wise Linear Modulation for Multi-Domain 3D Abdominal Segmentation: A Comparative

Juan F Garrido-Martínez¹, Javier M Garrido-López¹, Juan F Zapata-Pérez¹

¹Escuela Técnica Superior de Ingeniería Industrial, Campus Muralla del Mar, Universidad Politécnica de Cartagena, Member of European University of Technology EUT+, C/Doctor Fleming, s/n, 30202 Cartagena, Spain.

Sensors (Basel, Switzerland)

|July 15, 2026

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Feature-wise Linear Modulation (FiLM) metadata conditioning improved U-Net for 3D abdominal MRI segmentation, especially with heterogeneous data. However, FiLM did not consistently benefit nn-Net, highlighting architecture-dependent performance gains.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Automatic 3D abdominal organ segmentation in MRI is challenging due to data heterogeneity.
Differences in acquisition protocols and imaging characteristics degrade model generalization.
Metadata conditioning offers a potential solution to improve model adaptation.

Purpose of the Study:

To investigate the efficacy of Feature-wise Linear Modulation (FiLM) for metadata conditioning in 3D segmentation networks.
To compare the performance of U-Net and nnU-Net v2, with and without FiLM conditioning.
To analyze the impact of FiLM on multi-domain robustness using CHAOS and AMOS datasets.

Main Methods:

Implemented FiLM conditioning in U-Net and nnU-Net v2 architectures.

Keywords:

3D abdominal segmentation domain shift feature-wise linear modulation magnetic resonance imaging metadata conditioning nnU-Net

Related Experiment Videos

Last Updated: Jul 16, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Evaluated baseline and FiLM-conditioned models on CHAOS, AMOS, and combined datasets.

Assessed segmentation performance using Dice, HD95, and ASD metrics.

Analyzed the effect of post-processing on segmentation outputs.

Main Results:

FiLM conditioning showed favorable trends in U-Net, particularly in combined datasets, improving Dice, HD95, and ASD.
FiLM did not consistently benefit nnU-Net, and even reduced performance in some scenarios (e.g., AMOS).
Post-processing had a greater impact on U-Net than nnU-Net, indicating nnU-Net's inherent stability.

Conclusions:

FiLM can enhance generic architectures like U-Net for heterogeneous medical imaging segmentation tasks.
The benefits of metadata conditioning are architecture-dependent and not universally applicable.
nnU-Net v2 baseline demonstrated robust performance, requiring less benefit from additional conditioning mechanisms.