DRINet for Medical Image Segmentation

By Liang Chen, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert. 2018

301 citations recorded by the source corpus.

Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The U-Net architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. The U-Net architecture consists of standard convolution layers, pooling layers, and upsampling layers. These convolution layers learn representative features of input images and construct segmentations based on the features. However, the features learned by standard convolution layers are not distinctive when the differences among different categories are subtle in terms of intensity, location, shape, and size. In this paper, we propose a novel CNN architecture, called Dense-Res-Inception Net (DRINet), which addresses this challenging problem. The proposed DRINet consists of three blocks, namely a convolutional block with dense connections, a deconvolutional block with residual inception modules, and an unpooling block. Our proposed architecture outperforms the U-Net in three different challenging applications, namely multi-class segmentation of cerebrospinal fluid on brain CT images, multi-organ segmentation on abdominal CT images, and multi-class brain tumor segmentation on MR images.

Subfields: Computer Vision and Pattern Recognition, Neurology, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Debesh Jha
2. Pål Halvorsen
3. Michael Alexander Riegler
4. Dag Johansen
5. Haavard D. Johansen
6. Daniel Rueckert
7. Alexander Hammers
8. Colin P. Elkin

In Neurology

1. Michael Alexander Riegler
2. Pål Halvorsen
3. Colin P. Elkin
4. Joanna M. Wardlaw
5. Daniel Rueckert
6. Roger N. Gunn
7. Haavard D. Johansen
8. Debesh Jha

In Radiology, Nuclear Medicine and Imaging

1. Daniel Rueckert
2. Alexander Hammers
3. Michael Alexander Riegler
4. Pål Halvorsen
5. Colin P. Elkin
6. Roger N. Gunn
7. Haavard D. Johansen
8. Debesh Jha

Citations in the corpus

• GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks (2018, methodology): “The first dataset contains CT images with manually delineated CSF labels split into into 3 classes: cortical CSF, brain stem CSF and ventricular CSF. Data is split in the same way as in [3], using the same preprocessing and sampling procedures.”
• U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications (2020, background): “tumor detection [19], [44], [45], brain tissue mapping [46], cardiac segmentation [7], [47], lung nodule detection [48], human embryo segmentation [49], and ultrasound nerve segmentation [50].”
• DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation (2020, result): “[16] proposed Dense-Res-Inception Net (DRINET) for medical image segmentation and compared their results with FCN, U-Net, and ResUNet.”