Effect of Prior-based Losses on Segmentation Performance: A Benchmark

by   Rosana {EL JURDI}, et al.

Today, deep convolutional neural networks (CNNs) have demonstrated state-of-the-art performance for medical image segmentation, on various imaging modalities and tasks. Despite early success, segmentation networks may still generate anatomically aberrant segmentations, with holes or inaccuracies near the object boundaries. To enforce anatomical plausibility, recent research studies have focused on incorporating prior knowledge such as object shape or boundary, as constraints in the loss function. Prior integrated could be low-level referring to reformulated representations extracted from the ground-truth segmentations, or high-level representing external medical information such as the organ's shape or size. Over the past few years, prior-based losses exhibited a rising interest in the research field since they allow integration of expert knowledge while still being architecture-agnostic. However, given the diversity of prior-based losses on different medical imaging challenges and tasks, it has become hard to identify what loss works best for which dataset. In this paper, we establish a benchmark of recent prior-based losses for medical image segmentation. The main objective is to provide intuition onto which losses to choose given a particular task or dataset. To this end, four low-level and high-level prior-based losses are selected. The considered losses are validated on 8 different datasets from a variety of medical image segmentation challenges including the Decathlon, the ISLES and the WMH challenge. Results show that whereas low-level prior-based losses can guarantee an increase in performance over the Dice loss baseline regardless of the dataset characteristics, high-level prior-based losses can increase anatomical plausibility as per data characteristics.


page 5

page 6

page 7

page 8

page 9


High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey

Today, deep convolutional neural networks (CNNs) have demonstrated state...

A novel shape-based loss function for machine learning-based seminal organ segmentation in medical imaging

Automated medical image segmentation is an essential task to aid/speed u...

Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!

Standard losses for training deep segmentation networks could be seen as...

A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation

Despite the tremendous success of deep neural networks in medical image ...

Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts

Integrating high-level semantically correlated contents and low-level an...

Incorporating prior knowledge in medical image segmentation: a survey

Medical image segmentation, the task of partitioning an image into meani...

Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation

Convolutional Neural Networks (CNNs) work very well for supervised learn...

Please sign up or login with your details

Forgot password? Click here to reset