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3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Qi Dou, Hao Chen, Yueming Jin, Lequan Yu, Jing Qin, Pheng-Ann Heng
TL;DR
Automatic liver segmentation from CT volumes is important for hepatic diagnosis and treatment planning but is challenged by anatomical variation, low contrast, and pathology. The paper proposes a fully convolutional 3D deeply supervised network with CRF contour refinement, achieving competitive segmentation against state-of-the-art approaches and faster processing on MICCAI-SLiver07.
Problem
Automatic CT liver segmentation is important but difficult because of shape variation, low contrast with adjacent organs, and diverse pathologies, while volumetric context and high-level feature extraction remain open problems.
Method
The method combines a fully convolutional 3D DSN with hidden-layer deep supervision and fully connected CRF contour refinement.
Results
VOE of 5.42% and AvgD of 0.79mm were achieved on the testing set, with processing taking about 1.5 minutes per subject.
Takeaways & Limitations
The framework provides competitive liver segmentation accuracy with much faster processing speed and is presented as extendable to other medical volumetric segmentation applications with limited training data.
Abstract
from arXiv · showhide
Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment. In this paper, we present a novel 3D deeply supervised network (3D DSN) to address this challenging task. The proposed 3D DSN takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly, we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties, and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. On top of the high-quality score map produced by the 3D DSN, a conditional random field model is further employed to obtain refined segmentation results. We evaluated our framework on the public MICCAI-SLiver07 dataset. Extensive experiments demonstrated that our method achieves competitive segmentation results to state-of-the-art approaches with a much faster processing speed.
1 Introduction
Automatic liver segmentation from CT volumes is important for hepatic diagnosis and treatment planning but remains difficult because of anatomical variation, weak boundaries, and pathology. The paper proposes a 3D deeply supervised network to address unresolved challenges in volumetric context modeling and high-level feature extraction.
- Accurate liver segmentation supports computer-aided hepatic disease diagnosis and treatment planning.
- Automatic segmentation is motivated by manual annotation that is tedious, error-prone, and time-consuming.
- CT liver segmentation is challenging because of inter-patient shape variation, low contrast with adjacent organs, and varied pathologies.
- Leveraging volumetric contextual information and extracting powerful high-level representations for automatic liver segmentation remains an open problem.
- The proposed 3D DSN uses fully convolutional end-to-end learning, deep supervision, and CRF contour refinement for automatic liver segmentation.
2 Method
The method combines an end-to-end 3D fully convolutional architecture with hidden-layer supervision and CRF-based contour refinement. Deep supervision addresses difficult optimization, while the CRF refines ambiguous boundaries using network predictions and image information.
- 2.1 End-to-end 3D Fully Convolutional Architecture: The 3D DSN uses an 11-layer 3D architecture with convolution, pooling, deconvolution, softmax, and supervision branches at the third and sixth layers.
- 2.1 End-to-end 3D Fully Convolutional Architecture: Deconvolutional layers reconstruct dense predictions by up-scaling coarse feature volumes with trainable in-network kernels.
- 2.1 End-to-end 3D Fully Convolutional Architecture: The network inputs a large volume and directly outputs an equal-sized prediction score map through fully convolutional end-to-end learning and inference.
- 2.2 Deep Supervision for Learning Process: Per-voxel binary classification learning faces vanishing gradients, which can slow convergence and reduce discrimination capability, especially in 3D.
- 2.2 Deep Supervision for Learning Process: Deep supervision adds auxiliary dense-prediction losses from hidden layers, with simultaneous back-propagation from branch and final outputs.
- 2.3 Contour Refinement with CRF: A fully connected CRF refines ambiguous contours using unary potentials initialized from weighted network predictions and pairwise terms incorporating appearance and smoothness.
3 Experiments
Experiments evaluated the 3D DSN on MICCAI-SLiver07 using training-set leave-one-out analysis and held-out testing comparisons. The method outperformed 3D CNN and achieved strong accuracy with substantially faster processing than competing shape-modeling approaches.
- Dataset and setup: MICCAI-SLiver07 contains 30 contrast-enhanced CT scans, with 20 for training and 10 for testing.The implementation was trained from scratch with scheduled learning-rate and deep-supervision weight decay.
- Training-set evaluation: Training-set leave-one-out evaluation used VOE, VD, AvgD, RMSD, and MaxD, where lower absolute values indicate better segmentation.Table 1 evaluated the proposed methods under different settings using these five measures.
- Training-set evaluation: 3D DSN yielded superior training-set results to 3D CNN, indicating benefits from deep supervision for optimization and discrimination.The comparison was reported across the evaluation measures in Table 1.
- Qualitative results: The method delineated livers despite low contrast, inter-patient shape variation, and internal pathologies with abnormal appearance.Figure 3 shows ground truth in green and predictions in red across transverse, sagittal, and coronal views.
- Testing-set evaluation: 5.42% VOE and 0.79mm AvgD were achieved on the held-out testing set against top-ranking teams and published state-of-the-art approaches.These were identified as the two most important and commonly used liver-segmentation metrics.
- Testing-set evaluation: 1.5 mins per subject was the framework’s processing time, including 5s for 3D DSN and 87s for CRF.The paper reports this as faster than competing shape-modeling approaches, which commonly took several minutes.
4 Conclusion
The paper presents a 3D deeply supervised network for automatic liver segmentation in abnormal CT volumes, followed by CRF-based contour refinement. Results on SLiver07 were promising and substantially faster, while the approach is described as extensible to other volumetric segmentation applications with limited training data.
- 4 Conclusion: The 3D DSN generates high-quality score maps, and a conditional random field refines their contours.This combines the network’s volumetric segmentation output with a subsequent graphical-model refinement stage.
- 4 Conclusion: On SLiver07, the method achieved promising segmentation results with much faster processing speed.The conclusion summarizes the reported dataset-level outcome without specifying an additional metric.
- 4 Conclusion: The deep-learning method is described as general and readily extendable to other medical volumetric segmentation applications with limited training data.This is the paper’s stated broader applicability claim.