Published on Fri Jul 10 2020

Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation

Shen Wang, Kongming Liang, Yiming Li, Yizhou Yu, Yizhou Wang

Brain midline delineation can facilitate the clinical evaluation of brain pathology. The proposedCAR-Net explores more discriminative contextual features and a larger receptive field. The method requires fewer parameters and outperforms three state-of-the-art methods.

0
0
0
Abstract

Brain midline delineation can facilitate the clinical evaluation of brain midline shift, which plays an important role in the diagnosis and prognosis of various brain pathology. Nevertheless, there are still great challenges with brain midline delineation, such as the largely deformed midline caused by the mass effect and the possible morphological failure that the predicted midline is not a connected curve. To address these challenges, we propose a context-aware refinement network (CAR-Net) to refine and integrate the feature pyramid representation generated by the UNet. Consequently, the proposed CAR-Net explores more discriminative contextual features and a larger receptive field, which is of great importance to predict largely deformed midline. For keeping the structural connectivity of the brain midline, we introduce a novel connectivity regular loss (CRL) to punish the disconnectivity between adjacent coordinates. Moreover, we address the ignored prerequisite of previous regression-based methods that the brain CT image must be in the standard pose. A simple pose rectification network is presented to align the source input image to the standard pose image. Extensive experimental results on the CQ dataset and one inhouse dataset show that the proposed method requires fewer parameters and outperforms three state-of-the-art methods in terms of four evaluation metrics. Code is available at https://github.com/ShawnBIT/Brain-Midline-Detection.

Tue Feb 02 2021
Computer Vision
Atlas-aware ConvNetfor Accurate yet Robust Anatomical Segmentation
Convolutional networks (ConvNets) have achieved promising accuracy for various anatomical segmentation tasks. Despite the success, these methods can be sensitive to data appearance variations. This paper proposes to mitigate the challenge by enabling ConvNets'awareness of the underlying anatomical invariances among imaging scans.
0
0
0
Wed Jun 23 2021
Computer Vision
Conditional Deformable Image Registration with Convolutional Neural Network
Deep learning-based methods have shown promising results and runtime advantages in deformable image registration. This is because it involves training a substantial number of separate models with distinct hyperparameter values.
7
1
2
Tue Feb 25 2020
Machine Learning
Recalibrating 3D ConvNets with Project & Excite
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging. We extend existing 2D recalibration methods to 3D and propose a generic compress-process-recalibrate pipeline for easy comparison of such blocks.
0
0
0
Thu Sep 26 2019
Computer Vision
Dual-Stream Pyramid Registration Network
We propose a Dual-Stream Pyramid Registration Network for unsupervised 3D medical image registration. We design a two-stream architecture able to compute multi-scale registration fields from convolutional feature pyramids. The proposed Dual-PRNet is evaluated on two standard benchmarks for brain MRI registration.
0
0
0
Wed Sep 11 2019
Computer Vision
CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI
Many functional and structural neuroimaging studies call for accurate segmentation of different brain structures. Current automatic (multi-) atlas-based strategies often lack accuracy on difficult-to-segment brain structures and may take long processing times.
0
0
0
Thu Feb 27 2020
Computer Vision
Segmentation-based Method combined with Dynamic Programming for Brain Midline Delineation
Midline related pathological image features are crucial for evaluating severity of brain compression caused by stroke or traumatic brain injury. Most of the previous methods model the midline by localizing the anatomical points, which are hard to detect or even missing in severe cases.
0
0
0