Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography

Abstract

Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and they neglect the information from the contours and boundaries of the observed objects. In addition, shape boundaries can help to locate the positions of the observed objects, but most of the existing loss functions neglect the information from the boundaries. To overcome these shortcomings, this paper presents a new cascaded 2.5D fully convolutional networks (FCNs) learning framework to segment 3D medical images. A new boundary loss that incorporates distance, area, and boundary information is also proposed for the cascaded FCNs to learning more boundary and contour features from the 3D medical images. Moreover, an effective post-processing method is developed to further improve the segmentation accuracy. We verified the proposed method on LITS and 3DIRCADb datasets that include the liver and tumors. The experimental results show that the performance of the proposed method is better than existing methods with a Dice Per Case score of 74.5% for tumor segmentation, indicating the effectiveness of the proposed method.

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“…Third is to apply 2D CNNs with randomly oriented 2D cross sections. In the final step, 2.5D segmentation requires an additional post-processing step to generate 3D output ( Han et al, 2021 ). Although, the 3D CNN requires more resources and time for the model training, for the best performance, we use the 3D CNN in our implementation.…”

Recent advances in computerized imaging and its vital roles in liver disease diagnosis, preoperative planning, and interventional liver surgery: A review

“…With ensemble learning, tumors could be segmented with a 75% accuracy, compared to the approximately 65%-70% accuracy obtained by competing networks. The same accuracy of 74.5% was achieved by a 2.5D fully CNN whose loss function consisted of cross-entropy, a similarity coefficient, and a novel boundary loss function[ 88 ]. The latter was prescribed based on the boundary between segmented objects by means of logical morphology.…”

Advancing Human Action Recognition and Medical Image Segmentation using GRU Networks with V-Net Architecture

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