Automatic classification of lung nodules on MDCT images with the temporal subtraction technique

Yoshino, Yasumasa; Miyajima, Takahiro; Lu, Huimin; Tan, Joo Kooi; Kim, Hyoungseop; Murakami, Seiichi; Aoki, Takatoshi; Tachibana, Rie; Hirano, Yasushi; Shoji, K.

doi:10.1007/s11548-017-1598-1

Cited by 23 publications

(11 citation statements)

References 10 publications

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“…Non-rigid image registration methods have been proposed to reduce further subtraction artifacts on TS images (27)(28)(29)). Hereby we provide example of some of registration methods for reducing the subtraction artifacts due to normal structures, including blood vessels, in order to get precise subtraction of a pair of CT images.…”

Section: Non-rigid Image Registration Methods For Mdct Imagesmentioning

confidence: 99%

CT temporal subtraction: techniques and clinical applications

Aoki¹,

Kamiya²,

Lu³

et al. 2021

Quant Imaging Med Surg

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Section: Non-rigid Image Registration Methods For Mdct Imagesmentioning

confidence: 99%

CT temporal subtraction: techniques and clinical applications

Aoki¹,

Kamiya²,

Lu³

et al. 2021

Quant Imaging Med Surg

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“…In addition, a number of workflow steps, such as displaying and rearranging the images and retrieving additional comparison studies, requires extra time. 62 Even if the scans are registered perfectly, nodule size can vary up to 25% based on a number of patient factors, such as position, heart pulsation, and inspiration levels.…”

Section: Follow-upmentioning

confidence: 99%

Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications

Kadir²,

2020

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Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main application of these techniques has been the detection and classification of pulmonary nodules. In addition, there have been other less intensely researched applications, such as the diagnosis of interstitial lung disease, chronic obstructive pulmonary disease, and the detection of pulmonary emboli. Despite extensive literature on the use of convolutional neural networks in thoracic imaging over the last few decades, we are yet to see these systems in use in clinical practice. The article reviews current state-of-the-art applications of AI and in detection, classification, and follow-up of pulmonary nodules and how deep-learning techniques might influence these going forward. Finally, we postulate the impact of these advancements on the role of radiologists and the importance of radiologists in the development and evaluation of these techniques.

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“…According to World Health Organization, lung cancer is among the most frequent cancer overall and is the noteworthy cause of cancer-related mortality [1,2]. The earliest manifestation of lung cancer is pulmonary nodule [3]. With the wide availability and an increased ratio of chest CT scans, pulmonary nodules are being increasingly detected [3].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Maximum Intensity Projection and Volume Rendering in Detecting Pulmonary Nodules on Multidetector Computed Tomography

Naeem¹,

Darira²,

Ahmed³

et al. 2021

Cureus

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Introduction Lung cancer is the most common cancer overall, and the foremost cause of cancer-related mortality. Almost all lung cancers evolve from pulmonary nodules. As multidetector CT (MDCT) scanners are now widely available, there is an increased rate of detection of pulmonary nodules. It is of utmost importance to evaluate pulmonary nodules to rule out the possibility of neoplastic diseases. With advancements in technology, there are various manual and automatic analytic software providing a wide range of post-processing techniques. Maximum intensity projection (MIP) and volume rendering (VR) techniques have been analyzed previously regarding pulmonary nodules but there is a scarcity of data in terms of low-density nodules. This study aims to delineate the comparison and supremacy of both techniques in terms of low-density nodules. Methodology The current prospective study was conducted from June 2019 to June 2020 in the Radiology Department at Dr. Ziauddin Hospital, Karachi. Chest CT scans were performed on 16 slice MDCT (Alexion 16 Multi-slice, Toshiba Medical System Corporation, Houston, TX). A consultant radiologist of six years experience and a postgraduate trainee of three years experience analyzed each patient on a workstation (Vitrea 6.2.0, Vital Images, Minnetonka, MN). SPSS 23.0 (SPSS Inc., Chicago, IL) was incorporated for data analysis. Data were expressed in the median and interquartile range (IQR). Data collected for this study were analyzed using analyzing the median difference in nodule count using Wilcoxon’s signed-rank test. A p-value of <0.05 was considered significant. Results After informed consent, 236 patients were recruited for the study. MIP outperformed VR in terms of nodule detection and low-density nodules at each evaluated slab thicknesses (p<0.001). A 10-mm MIP was superior to all other techniques in terms of detection of pulmonary nodules and low-density nodules (p<0.001). MIP was also considered an easier technique as there was excellent inter-rater reliability and agreement. Conclusion This study is robust evidence regarding the supremacy of MIP. MIP outperformed VR on every slab thicknesses. The 10-mm MIP technique was superior to all others evaluated and was recorded to be an easier analyzing technique.

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Automatic classification of lung nodules on MDCT images with the temporal subtraction technique

Abstract: The experimental results indicate the efficiency of our segmentation method.

Cited by 23 publications

References 10 publications

CT temporal subtraction: techniques and clinical applications

CT temporal subtraction: techniques and clinical applications

Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications

Comparison of Maximum Intensity Projection and Volume Rendering in Detecting Pulmonary Nodules on Multidetector Computed Tomography

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