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International Journal of Computer Assisted Radiology and Surgery

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Open Access 01-01-2021 | Computed Tomography | Original Article

Toward reliable automatic liver and tumor segmentation using convolutional neural network based on 2.5D models

Authors: Girindra Wardhana, Hamid Naghibi, Beril Sirmacek, Momen Abayazid

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 1/2021

Abstract

Purpose

We investigated the parameter configuration in the automatic liver and tumor segmentation using a convolutional neural network based on 2.5D model. The implementation of 2.5D model shows promising results since it allows the network to have a deeper and wider network architecture while still accommodates the 3D information. However, there has been no detailed investigation of the parameter configurations on this type of network model.

Methods

Some parameters, such as the number of stacked layers, image contrast, and the number of network layers, were studied and implemented on neural networks based on 2.5D model. Networks are trained and tested by utilizing the dataset from liver and tumor segmentation challenge (LiTS). The network performance was further evaluated by comparing the network segmentation with manual segmentation from nine technical physicians and an experienced radiologist.

Results

Slice arrangement testing shows that multiple stacked layers have better performance than a single-layer network. However, the dice scores start decreasing when the number of stacked layers is more than three layers. Adding higher number of layers would cause overfitting on the training set. In contrast enhancement test, implementing contrast enhancement method did not show a statistically significant different to the network performance. While in the network layer test, adding more layers to the network architecture does not always correspond to the increasing dice score result of the network.

Conclusions

This paper compares the performance of the network based on 2.5D model using different parameter configurations. The result obtained shows the effect of each parameter and allow the selection of the best configuration in order to improve the network performance in the application of automatic liver and tumor segmentation.

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Title: Toward reliable automatic liver and tumor segmentation using convolutional neural network based on 2.5D models
Authors: Girindra Wardhana
Hamid Naghibi
Beril Sirmacek
Momen Abayazid
Publication date: 01-01-2021
Publisher: Springer International Publishing
Keywords: Computed Tomography
Computed Tomography
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 1/2021
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-020-02292-y

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Toward reliable automatic liver and tumor segmentation using convolutional neural network based on 2.5D models

Abstract

Purpose

Methods

Results

Conclusions

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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