Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning

Nakano, Risa; Arimura, Hidetaka; Haekal, Mohammad; Ohga, Saiji

doi:10.1117/12.2521509

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Many variations and small modifications of the original UNet architecture have been proposed [33,[152][153][154][155][156][157][158][159][160][161][162][163][164][165], including 3D variations coined VNet or 3D UNet [166][167][168][169][170][171][172][173][174][175][176][177][178][179] that are able to process cube patches. Some research fuses features or results from multiple views (2.5D) or multiple 2D and 3D networks attempting to capture information from different angles and dimensionalities [180][181][182][183][184][185][186][187][188][189][190].…”

Section: Deep Learningmentioning

confidence: 99%

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

et al. 2022

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Objectives: Automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods. Methods: We used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation. Results: We organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field. Conclusions: We noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general.

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