DeepAI
Log In Sign Up

Trident Segmentation CNN: A Spatiotemporal Transformation CNN for Punctate White Matter Lesions Segmentation in Preterm Neonates

10/22/2019
by   Yalong Liu, et al.
0

Accurate segmentation of punctate white matter lesions (PWML) in preterm neonates by an automatic algorithm can better assist doctors in diagnosis. However, the existing algorithms have many limitations, such as low detection accuracy and large resource consumption. In this paper, a novel spatiotemporal transformation deep learning method called Trident Segmentation CNN (TS-CNN) is proposed to segment PWML in MR images. It can convert spatial information into temporal information, which reduces the consumption of computing resources. Furthermore, a new improved training loss called Self-balancing Focal Loss (SBFL) is proposed to balance the loss during the training process. The whole model is evaluated on a dataset of 704 MR images. Overall the method achieves median DSC, sensitivity, specificity, and Hausdorff distance of 0.6355, 0.7126, 0.9998, and 24.5836 mm which outperforms the state-of-the-art algorithm. (The code is now available on https://github.com/YalongLiu/Trident-Segmentation-CNN)

READ FULL TEXT
08/04/2021

Automatic hemisphere segmentation in rodent MRI with lesions

We present MedicDeepLabv3+, a convolutional neural network that is the f...
10/19/2020

Brain Atlas Guided Attention U-Net for White Matter Hyperintensity Segmentation

White Matter Hyperintensities (WMH) are the most common manifestation of...
11/27/2020

Multi-task MR Imaging with Iterative Teacher Forcing and Re-weighted Deep Learning

Noises, artifacts, and loss of information caused by the magnetic resona...
11/30/2017

MR image reconstruction using the learned data distribution as prior

MR image reconstruction from undersampled data exploits priors to compen...