Brief Review — Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing
U-Net as Segmentation Network + RF as Post Processing
Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing,
FCN+RF, by Fraunhofer Institute for Medical Image Computing MEVIS, Radboud University Medical Center, Jacobs University, and University of Bremen,
2018 Nature Sci. Rep., Over 170 Citations (Sik-Ho Tsang @ Medium)
Medical Image Analysis, Medical Imaging, Image Segmentation, U-Net
- A fully automatic method for liver tumor segmentation in CT images is developed based on a 2D fully convolutional neural network with an object-based postprocessing step.
1.1. Segmentation Model Using U-Net
- U-Net like model is used, which works on four resolution levels allowing for learning of local and global features.
- The network contains long skip connections, and also short skip connections.
- Each convolutional layer uses 3×3 filter size and is followed by a batch normalization and a ReLU activation function.
- Dropout (p = 0.5) is used before each convolution in the upscaling path to prevent the network from overfitting.
1.2. Object-based Postprocessing Using RF
- Based on the training data we observed that some neural network outputs corresponded to false positives, which could easily be identified by their shape and location.
- A post-processing step is added, which employs a model classifying tumor objects (computed as 3D connected components of the FCN output) into true (TP) and false positives (FP).
- A conventional random forest classifier (RF) is trained, with 256 trees using 36 hand-crafted features carrying information about underlying image statistics, tumor shape and its distance to the liver boundary.
- Because they work well with moderate numbers of training samples and varying feature value distributions.
- LiTS: Ground-truth labels provided by dataset.
- MTRA: An expert asked by authors to annotate again.
- The neural network was able to detect 47% and 72% of all tumors present in the MTRA and LiTS annotations, respectively.
- The dice/case and dice/correspondence was 0.53 and 0.72 for the MTRA reference and 0.51 and 0.65 for LiTS reference.
- The RF classifier allowed for a 85% reduction of false positives and had 87% accuracy on test cases. The improvement for Dice per correspondence was significant.
- At that moment, it is ranking third at the MICCAI 2017 LiTS round (leaderboard user name hans.meine). The submission scored 0.68 and 0.96 dice/case for tumor and liver segmentation, respectively.
- The proposed method needs on average 67s for one case: 43, 16 and 8s for liver segmentation, tumor segmentation and FP filtering, respectively.
- Some visualizations are shown above.
[2018 Nature Sci. Rep.] [U-Net+RF]
Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing
4.2. Biomedical Image Segmentation
2015 … 2018 … [U-Net+RF] 2020 [MultiResUNet] [UNet 3+] [Dense-Gated U-Net (DGNet)] [Non-local U-Net] [SAUNet] [SDM] [DIU-Net] [Chen FCVM’20] 2021 [Expanded U-Net]