Review: Cascaded 3D U-Net — Multi-Organ Segmentation (Biomedical Image Segmentation)

3D Convolutions for Simultaneous Artery, Portal Vein, Liver, Spleen, Stomach, Gallbladder, and Pancreas Segmentation

  • In the first stage, an FCN model is trained to roughly delineate the organs of interest.
  • In the second stage, an FCN model is trained to have more detailed segmentation of the organs.

Outline

  1. Coarse-to-Fine Cascaded U-Net
  2. Loss Function
  3. Training & Validation & Testing
  4. Comparison with State-of-the-art Approaches

1. Coarse-to-Fine Cascaded U-Net

Multi-stage cascaded training scheme
C1 Candidate Region (Red) input to Stage 1 3D U-Net (Left), C2 Candidate Region (Red) input to Stage 2 3D U-Net (Right)
  • The first-stage FCN sees around 40% of the voxels as C1 Candidate Region using only a simple mask of the body created by thresholding the image.
  • C2 Candidate Region is output from the first-stage FCN.
  • In second-stage FCN, the amount of the image’s voxels is reduced by around 10%.
  • This step narrows down and simplifies the search space for the FCN.
3D U-Net
  • In this paper, the 2-stage FCNs used are 3D U-Net.
  • (For details about 3D U-Net, Please feel free to read my review about it.)
  • At the end of the network, the last layer contains a 1×1×1 convolution that reduces the number of output channels to the number of class labels (K=8, 7 organs plus 1 background) and a size of 44×44×28 of each channel.

2. Loss Function

  • Ni are the number of voxels within one class in Ln.
  • NC is the number of voxels within candidate region C1 or C2.
  • λi is the value based on Ni and NC, i.e. based on the occurrence frequency of the class. And summing all λi equals to 1.
  • Simply speaking, the loss is the weighted voxel-wise cross-entropy loss.

3. Training & Validation & Testing

3.1 Training & Validation

  • Dataset: 331 contrast-enhanced abdominal clinical CT images.
  • Each CT volume consists of 460 — 1177 slices of 512×512 pixels. The voxel dimensions are [0.59–0.98, 0.59–0.98, 0.5–1.0] mm.
  • A random split of 281/50 patients is used for training and validating the network.
Various examples of plausible random deformation
  • Data Augmentation: Smooth B-spline deformations, random rotations.
  • 200,000 iterations in the first stage and 115,000 in the second.
Dics Similiary Score on Validation Set
  • With 2 stages, dice score is improved from 71.7% to 79.2%.
  • On average, a 7.5% improvement in Dice scores per organ is achieved.
Example of the validation set
  • The segmentation is quite good as shown above.

3.2 Testing

  • Dataset: It originates from a different hospital, scanner, and research study with gastric cancer patients. 150 abdominal CT scans were acquired.
  • Each CT volume consists of 263 — 1061 slices of 512×512 pixels. Voxel dimensions are [0.55–0.82, 0.55–0.82, 0.4–0.80] mm.
  • The pancreas, liver, and spleen were semi-automatically delineated by three trained researchers and confirmed by a clinician.
Dics Similiary Score on Test Set
  • The dice similarity score is improved at stage 2.
  • With overlapping, the score is even higher.
Example of the test set

3.3. Computation

  • Training on 281 cases can take 2–3 days for 200-k iterations on a NVIDIA GeForce GTX TITAN X with 12 GB memory.
  • On 150 cases of the test set, the processing time for each volume was 1.4–3.3 minutes for each stage, depending on the size of the candidate regions.

4. Comparison with State-of-the-art Approaches

Comparison with State-of-the-art Approaches
  • Without any post-processing, the proposed approach outperforms others such as random forest (RF) approach or other FCN approaches.
Multi-organ segmentation result. Each color represents an organ region on the unseen whole torso test set

Reference

[2017 arXiv] [Cascaded 3D U-Net]
Hierarchical 3D fully convolutional networks for multi-organ segmentation

My Previous Reviews

Image Classification [LeNet] [AlexNet] [Maxout] [NIN] [ZFNet] [VGGNet] [Highway] [SPPNet] [PReLU-Net] [STN] [DeepImage] [SqueezeNet] [GoogLeNet / Inception-v1] [BN-Inception / Inception-v2] [Inception-v3] [Inception-v4] [Xception] [MobileNetV1] [ResNet] [Pre-Activation ResNet] [RiR] [RoR] [Stochastic Depth] [WRN] [ResNet-38] [Shake-Shake] [FractalNet] [Trimps-Soushen] [PolyNet] [ResNeXt] [DenseNet] [PyramidNet] [DRN] [DPN] [Residual Attention Network] [DMRNet / DFN-MR] [IGCNet / IGCV1] [MSDNet] [ShuffleNet V1] [SENet] [NASNet] [MobileNetV2]

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