Lung-Nodule Segmentation Using a Convolutional Neural Network with the U-Net Architecture

Hernández-Solis, Vicente; Téllez-Velázquez, Arturo; Orantes-Molina, Antonio; Cruz-Barbosa, Raúl

doi:10.1007/978-3-030-77004-4_32

Cited by 1 publication

(1 citation statement)

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“…For example, "professors" <= 1 means the nodules that are annotated at least a professor. As you can see in the Table 5 and Table 6, our best DSC result (0.741) is lower than other state-of-the-art results (more than 0.82) include [14]. This paper we focus instead on demonstrating the benefit of lung-range standardization rather than obtaining state-of-the-art result.…”

Section: Comparisonmentioning

confidence: 83%

CNN based pulmonary nodule segmentation using lung-range-standardization

Chung-Hyok

Cholnam

2023

Preprint

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Lung cancer is one of the most fatal disease with high lethality. In general lung cancers are diagnosed by radiologists. But checking radiological image is a very toilsome work for radiologists because it requires long time practice and high concentration. So, many computer-aided diagnosis (CAD) systems were introduced to cooperate with radiologists and nowadays lots of CAD systems based upon deep learning exceed human experts in diagnosing accuracy. And the remarkable thing is that the much of progress has been made in designing architectures. But, in this paper, a new pre-processing method (lung-range-standardization) is proposed in order to improve the general accuracy of lung-related diagnosis systems and to increase the utility of LIDC dataset. And the efficiency of the proposed pre-processing method is validated through comparison between the nodule segmentation model trained using lung-range-standardization and the nodule segmentation model, which is trained without lung-range-standardization.

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Section: Comparisonmentioning

confidence: 83%