Benefits of Linear Conditioning for Segmentation using Metadata

by   Andréanne Lemay, et al.

Medical images are often accompanied by metadata describing the image (vendor, acquisition parameters) and the patient (disease type or severity, demographics, genomics). This metadata is usually disregarded by image segmentation methods. In this work, we adapt a linear conditioning method called FiLM (Feature-wise Linear Modulation) for image segmentation tasks. This FiLM adaptation enables integrating metadata into segmentation models for better performance. We observed an average Dice score increase of 5.1 spinal cord tumor segmentation when incorporating the tumor type with FiLM. The metadata modulates the segmentation process through low-cost affine transformations applied on feature maps which can be included in any neural network's architecture. Additionally, we assess the relevance of segmentation FiLM layers for tackling common challenges in medical imaging: training with limited or unbalanced number of annotated data, multi-class training with missing segmentations, and model adaptation to multiple tasks. Our results demonstrated the following benefits of FiLM for segmentation: FiLMed U-Net was robust to missing labels and reached higher Dice scores with few labels (up to 16.7



page 3

page 12


Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?

Despite recent improvements in medical image segmentation, the ability t...

Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices

Deep learning techniques, particularly convolutional neural networks, ha...

Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation

In clinical practice, regions of interest in medical imaging often need ...

Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation

U-Net has achieved huge success in various medical image segmentation ch...

Robustifying deep networks for image segmentation

Purpose: The purpose of this study is to investigate the robustness of a...

ivadomed: A Medical Imaging Deep Learning Toolbox

ivadomed is an open-source Python package for designing, end-to-end trai...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.