Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy

Deep neural networks with multilevel connections process input data in complex ways to learn the information.A networks learning efficiency depends not only on the complex neural network architecture but also on the input training images.Medical image segmentation with deep neural networks for skull stripping or tumor segmentation from magnetic resonance images enables learning both global and local features of the images.Though medical images are collected in a controlled environment,there may be artifacts or equipment based variance that cause inherent bias in the input set.In this study, we investigated the correlation between the image quality metrics of MR images with the neural network segmentation accuracy.For that we have used the 3D DenseNet architecture and let the network trained on the same input but applying different methodologies to select the training data set based on the IQM values.The difference in the segmentation accuracy between models based on the random training inputs with IQM based training inputs shed light on the role of image quality metrics on segmentation accuracy.By running the image quality metrics to choose the training inputs,further we may tune the learning efficiency of the network and the segmentation accuracy.

PocketNet: A Smaller Neural Network for 3D Medical Image Segmentation

Overparameterized deep learning networks have shown impressive performance in the area of automatic medical image segmentation. However, they achieve this performance at an enormous cost in memory, runtime, and energy. A large source of overparameterization in modern neural networks results from doubling the number of feature maps with each downsampling layer. This rapid growth in the number of parameters results in network architectures that require a significant amount of computing resources, making them less accessible and difficult to use. By keeping the number of feature maps constant throughout the network, we derive a new CNN architecture called PocketNet that achieves comparable segmentation results to conventional CNNs while using less than 3% of the number of parameters.