End-to-End Boundary Aware Networks for Medical Image Segmentation
Hatamizadeh, A.; Terzopoulos, D.; Myronenko, A.
Show abstract
Fully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class masks that achieve semantic image segmentation. In medical image analysis, however, expert manual segmentation often relies on the boundaries of anatomical structures of interest. We propose boundary aware CNNs for medical image segmentation. Our networks are designed to account for organ boundary information, both by providing a special network edge branch and edge-aware loss terms, and they are trainable end-to-end. We validate their effectiveness on the task of brain tumor segmentation using the BraTS 2018 dataset. Our experiments reveal that our approach yields more accurate segmentation results, which makes it promising for more extensive application to medical image segmentation.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- STAMP: Simultaneous Training and Model Pruning for Low Data Regimes in Medical Image Segmentation 96%
- Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images 94%
- A Framework for Falsifiable Explanations of Machine Learning Models with an Application in Computational Pathology 93%
Similar papers in this journal
- SN-FPN: Self-attention Nested Feature Pyramid Network for Digital Pathology Image Segmentation 95%
- Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals 94%
- Cell segmentation without annotation by unsupervised domain adaptation based on cooperative self-learning 94%
Similar papers in this journal
- GLAPAL-H: Global, Local, And Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI 97%
- Maximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks 95%
- Saak Transform-Based Machine Learning for Light-Sheet Imaging of Cardiac Trabeculation 92%
Similar papers in this journal
- Enhancing Breast Ultrasound Segmentation through Fine-tuning and Optimization Techniques: Sharp Attention UNet 95%
- Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement 94%
- Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.