Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest

Liu, Shiyuan; Hoffman, Joanne; Zhao, Jocelyn; Yao, Jianhua; Lü, Le; Kim, Lauren; Türkbey, Evrim; Summers, Ronald M.

doi:10.1118/1.4954009

Cited by 49 publications

(32 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TA can potentially increase the accuracy of nodal staging as lymph nodes can be auto-mapped and identified. 83 TA has been shown to predict whether a lymph node is malignant or not in biopsy proven nodes, with a sensitivity of 81% and specificity of 80% (AUC = 0.87, p < 0.0001). This was achieved using a combination of three textural features: entropy, grey level non-uniformity and run length non-uniformity with three features of shape, which assessed the degree to which a lymph node was circular.…”

Section: Pre-treatment Textural Analysis (Ta)mentioning

confidence: 99%

Clinical applications of textural analysis in non-small cell lung cancer

Phillips

Ajaz

Ezhil

et al. 2017

The British Journal of Radiology

View full text Add to dashboard Cite

Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed "radiomics" and includes semantic and agnostic approaches. Textural analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour. TA is an attractive tool as large existing image sets can be submitted to diverse techniques for data processing, presentation, interpretation and hypothesis testing with annotated clinical outcomes. There is a growing anthology of published data using different TA techniques to differentiate between benign and malignant lung nodules, differentiate tissue subtypes of lung cancer, prognosticate and predict outcome and treatment response, as well as predict treatment side effects and potentially aid radiotherapy planning. The aim of this systematic review is to summarize the current published data and understand the potential future role of TA in managing lung cancer.

show abstract

Section: Pre-treatment Textural Analysis (Ta)mentioning

confidence: 99%

Clinical applications of textural analysis in non-small cell lung cancer

Phillips

Ajaz

Ezhil

et al. 2017

The British Journal of Radiology

View full text Add to dashboard Cite

show abstract

“…Similarly, Google’s DeepMind software is being used to test the feasibility of the automated grading of digital fundus photographs using optical coherence tomography [19]. Recently, AI has been used to predict genetic variations in low-grade gliomas [20], identify genetic phenotypes in small cell lung carcinoma [21], decrease false-positive rates in screening mammography computer-aided detection [22], improve pathologic mediastinal lymph node detection [23], and automatically perform bone age assessment [24]. These examples demonstrate the influence of AI in medicine.…”

Section: Introductionmentioning

confidence: 99%

Physician Confidence in Artificial Intelligence: An Online Mobile Survey

Oh¹,

Kim²,

Choi³

et al. 2019

J Med Internet Res

237

174

View full text Add to dashboard Cite

Background It is expected that artificial intelligence (AI) will be used extensively in the medical field in the future. Objective The purpose of this study is to investigate the awareness of AI among Korean doctors and to assess physicians’ attitudes toward the medical application of AI. Methods We conducted an online survey composed of 11 closed-ended questions using Google Forms. The survey consisted of questions regarding the recognition of and attitudes toward AI, the development direction of AI in medicine, and the possible risks of using AI in the medical field. Results A total of 669 participants completed the survey. Only 40 (5.9%) answered that they had good familiarity with AI. However, most participants considered AI useful in the medical field (558/669, 83.4% agreement). The advantage of using AI was seen as the ability to analyze vast amounts of high-quality, clinically relevant data in real time. Respondents agreed that the area of medicine in which AI would be most useful is disease diagnosis (558/669, 83.4% agreement). One possible problem cited by the participants was that AI would not be able to assist in unexpected situations owing to inadequate information (196/669, 29.3%). Less than half of the participants(294/669, 43.9%) agreed that AI is diagnostically superior to human doctors. Only 237 (35.4%) answered that they agreed that AI could replace them in their jobs. Conclusions This study suggests that Korean doctors and medical students have favorable attitudes toward AI in the medical field. The majority of physicians surveyed believed that AI will not replace their roles in the future.

show abstract

“…For the latter, machine learning could be employed to build cone photoreceptor classifiers to recover false negatives, as has been previously demonstrated for kidney cancer, 59 prostate cancer, 60 and lymph node identification. 61 …”

Section: Discussionmentioning

confidence: 99%

Automated Photoreceptor Cell Identification on Nonconfocal Adaptive Optics Images Using Multiscale Circular Voting

Liu

Jung

Dubra

et al. 2017

Invest. Ophthalmol. Vis. Sci.

View full text Add to dashboard Cite

PurposeAdaptive optics scanning light ophthalmoscopy (AOSLO) has enabled quantification of the photoreceptor mosaic in the living human eye using metrics such as cell density and average spacing. These rely on the identification of individual cells. Here, we demonstrate a novel approach for computer-aided identification of cone photoreceptors on nonconfocal split detection AOSLO images.MethodsAlgorithms for identification of cone photoreceptors were developed, based on multiscale circular voting (MSCV) in combination with a priori knowledge that split detection images resemble Nomarski differential interference contrast images, in which dark and bright regions are present on the two sides of each cell. The proposed algorithm locates dark and bright region pairs, iteratively refining the identification across multiple scales. Identification accuracy was assessed in data from 10 subjects by comparing automated identifications with manual labeling, followed by computation of density and spacing metrics for comparison to histology and published data.ResultsThere was good agreement between manual and automated cone identifications with overall recall, precision, and F1 score of 92.9%, 90.8%, and 91.8%, respectively. On average, computed density and spacing values using automated identification were within 10.7% and 11.2% of the expected histology values across eccentricities ranging from 0.5 to 6.2 mm. There was no statistically significant difference between MSCV-based and histology-based density measurements (P = 0.96, Kolmogorov-Smirnov 2-sample test).ConclusionsMSCV can accurately detect cone photoreceptors on split detection images across a range of eccentricities, enabling quick, objective estimation of photoreceptor mosaic metrics, which will be important for future clinical trials utilizing adaptive optics.

show abstract

Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest

Cited by 49 publications

References 61 publications

Clinical applications of textural analysis in non-small cell lung cancer

Clinical applications of textural analysis in non-small cell lung cancer

Physician Confidence in Artificial Intelligence: An Online Mobile Survey

Automated Photoreceptor Cell Identification on Nonconfocal Adaptive Optics Images Using Multiscale Circular Voting

Contact Info

Product

Resources

About