Toward reliable automatic liver and tumor segmentation using convolutional neural network based on 2.5D models
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ORIGINAL ARTICLE
Toward reliable automatic liver and tumor segmentation using convolutional neural network based on 2.5D models Girindra Wardhana1
· Hamid Naghibi1 · Beril Sirmacek1 · Momen Abayazid1
Received: 12 January 2020 / Accepted: 4 November 2020 © The Author(s) 2020
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. Keywords CT image · Convolutional neural network · Deep learning · Image segmentation · Liver tumor
Introduction Liver cancer is among the leading causes of cancer death globally (2015:810.000) with increasing diagnosed cases (2015:854.000) [1]. Prevention and treatment of liver disease are urgent since an early action can significantly reduce the progression of the disease. Clinicians utilize medical imaging to provide an early diagnosis by providing a clear picture of the possible lesion inside the patient body. Information such as size, shape, and the exact location of the lesions are obtained by segmentation. In medical terms, image seg-
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Girindra Wardhana [email protected] Department of Robotics and Mechatronics, The Faculty of Electrical Engineering, Mathematics and Computer Science, Technical Medical Centre, University of Twente, 7522 NB Enschede, The Netherlands
mentation helps in separating th
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