Automatic liver segmentation by integrating fully convolutional networks into active contour models

Abstract

Purpose Automatic and accurate three‐dimensional (3D) segmentation of liver with severe diseases from computed tomography (CT) images is a challenging task. Fully convolutional networks (FCNs) have emerged as powerful tools for automatic semantic segmentation, with multiple potential applications in medical imaging. However, the use of a large receptive field and multiple pooling layers in the network leads to poor localization around object boundaries. The network usually makes pixel‐wise prediction independently, making it difficult to respect local label consistency and enforce the smoothness of the object boundary. Methods We have developed an automatic liver segmentation method based on a novel framework that integrates fully convolutional network predictions into active contour models (ACM). We use only a single network architecture to generate a pixel label map containing spatial regional information (foreground and background) as well as layered boundary information. We exploit the structured network outcome to define an external constraint force of active contour models. A unique property of the designed force is that both its strength and direction are adaptive to its position and relative distance to the object boundary. The resulting integrated active contour models have the advantages of incorporating both high‐level and low‐level image information simultaneously, while enforcing the smoothness of the contour. Because the external constraint force can push the evolving contour to the liver boundary and exists everywhere in the image domain, it allows us to place the initial contour far away from the liver boundary. It potentially allows us to control the evolution of the contour in order to preserve the topology of the liver. Results We have trained and evaluated our model on 73 liver CT scans from a clinic study. The integrated ACM model yields mean dice coefficients (DICE) 95.8 ± 1.4 (%). Without further fine‐tuning the network weights for two independent datasets, it yields mean DICE 96.2 ± 0.9 (%) for the SLIVER07 training dataset, and mean DICE 94.3 ± 2.7 (%) for the LiTS training dataset. In comparison with FCN alone model, the integrated ACM model yields improvements in terms of surface distance and DICE values for almost all the cases. Furthermore, the initialization of the active contour can be very far away from the liver boundary. Conclusions Experimental results for segmenting livers (with severe diseases on CT images resulting in shape and density abnormalities) have revealed that our proposed model improves segmentation results in comparison with FCN alone. Without further fine‐tuning the network weights for two independent datasets, the model is capable of handling image variations from different datasets due to its inherent deformable nature. It is relatively easy to integrate more advanced (either existing or future) FCN architecture into our framework to further improve the segmentation performance.

Automatic liver segmentation using 3D convolutional neural networks with a hybrid loss function

“…Since there are few results submitted with available references on the CHAOS challenge, we compared our results with other well‐known methods such as Guo et al, 24 Li et al, 42 Novikov et al, 43 and Qin et al, 26 which are validated on different datasets in Table IX. Guo et al 24 integrated fully convolutional network predictions into active contour models for automatic liver segmentation. Li et al 42 integrated shape based initialization and the deformable graph cut method with incorporation of shape constraints.…”

“…Many CNN-based methods take further postprocessing steps to improve the segmentation results, such as CRF, graph cut, active contours, and surface evolution. 19,[21][22][23][24] To verify the effectiveness of postprocessing steps, we compared the FIG. 5.…”

“…More recently, some other liver segmentation methods combined with CNN have been proposed. Guo et al 24 developed an automatic liver segmentation method based on a novel framework that integrated FCN predictions into active contour models (ACM). Rather than directly learning and producing a binary label map from FCN, they proposed to learn rich information by FCN, and to transfer this information to an external constraint force of ACM.…”

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