Computer-aided Detection of Brain Metastases in T1-weighted MRI for                     Stereotactic Radiosurgery Using Deep Learning Single-Shot                     Detectors

Zhou, Zijian; Sanders, Jeremiah; Johnson, Jason M.; Gule-Monroe, Maria; Chen, Melissa; Briere, Tina Marie; Wang, Yan; Son, Jong Bum; Pagel, Mark; Li, Jing; Ma, Jingfei

doi:10.1148/radiol.2020191479

Cited by 91 publications

(107 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The high predictive potential of our network for brain metastases might present an integral part of fully automated diagnosis in the near future, complementing the model with preoperatively diagnosed metastases via computer-aided detection in magnetic resonance imaging (22,23). Izadyyazdanabadi et al showed promising results applying neural network models to categorize CLE data of brain neoplasms into diagnostic and non-diagnostic images, though not specifying the actual tumor entity (24).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Neural Network for Differentiation of Brain Tumor Tissue Displayed by Confocal Laser Endomicroscopy

Ziebart

Stadniczuk²,

Roos

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

BackgroundReliable on site classification of resected tumor specimens remains a challenge. Implementation of high-resolution confocal laser endoscopic techniques (CLEs) during fluorescence-guided brain tumor surgery is a new tool for intraoperative tumor tissue visualization. To overcome observer dependent errors, we aimed to predict tumor type by applying a deep learning model to image data obtained by CLE.MethodsHuman brain tumor specimens from 25 patients with brain metastasis, glioblastoma, and meningioma were evaluated within this study. In addition to routine histopathological analysis, tissue samples were stained with fluorescein ex vivo and analyzed with CLE. We trained two convolutional neural networks and built a predictive level for the outputs.ResultsMultiple CLE images were obtained from each specimen with a total number of 13,972 fluorescein based images. Test accuracy of 90.9% was achieved after applying a two-class prediction for glioblastomas and brain metastases with an area under the curve (AUC) value of 0.92. For three class predictions, our model achieved a ratio of correct predicted label of 85.8% in the test set, which was confirmed with five-fold cross validation, without definition of confidence. Applying a confidence rate of 0.999 increased the prediction accuracy to 98.6% when images with substantial artifacts were excluded before the analysis. 36.3% of total images met the output criteria.ConclusionsWe trained a residual network model that allows automated, on site analysis of resected tumor specimens based on CLE image datasets. Further in vivo studies are required to assess the clinical benefit CLE can have.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Deep Neural Network for Differentiation of Brain Tumor Tissue Displayed by Confocal Laser Endomicroscopy

Ziebart

Stadniczuk²,

Roos

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…The main objectives of the papers involved: pathological or molecular classification of the tumors (N = 7) [38][39][40][41][42][43][44] solely detection of tumor in a Computer Aided Diagnosis (CAD) fashion (N = 1) [45] and the combination of detection and segmentation of the lesions (N = 4) [46][47][48][49]. The number of patients used in the studies span from a minimum of 33 patients [47] to a maximum of 266 patients [49]. Sert et al [48] used a publicly available dataset from the cancer imaging archive (TCIA; TCGA-GBM), which includes more than 500 samples; however the authors selected 100 positives (including at least a tumor) and 100 negative samples (healthy subjects) to train the Convolutional Neural network (CNN; ResNet architecture) .…”

Section: Diagnosismentioning

confidence: 99%

Current applications of deep-learning in neuro-oncological MRI

et al. 2021

View full text Add to dashboard Cite

Magnetic Resonance Imaging (MRI) provides an essential contribution in the screening, detection, diagnosis, staging, treatment and follow-up in patients with a neurological neoplasm. Deep learning (DL), a subdomain of artificial intelligence has the potential to enhance the characterization, processing and interpretation of MRI images. The aim of this review paper is to give an overview of the current state-of-art usage of DL in MRI for neuro-oncology. Methods: We reviewed the Pubmed database by applying a specific search strategy including the combination of MRI, DL, neuro-oncology and its corresponding search terminologies, by focussing on Medical Subject Headings (Mesh) or title/abstract appearance. The original research papers were classified based on its application, into three categories: technological innovation, diagnosis and follow-up.Results: Forty-one publications were eligible for review, all were published after the year 2016. The majority (N = 22) was assigned to technological innovation, twelve had a focus on diagnosis and seven were related to patient follow-up. Applications ranged from improving the acquisition, synthetic CT generation, autosegmentation, tumor classification, outcome prediction and response assessment. The majority of publications made use of standard (T1w, cT1w, T2w and FLAIR imaging), with only a few exceptions using more advanced MRI technologies. The majority of studies used a variation on convolution neural network (CNN) architectures. Conclusion:Deep learning in MRI for neuro-oncology is a novel field of research; it has potential in a broad range of applications. Remaining challenges include the accessibility of large imaging datasets, the applicability across institutes/vendors and the validation and implementation of these technologies in clinical practise.

show abstract

“…The structure has a truncated part of VGG 16 with an additional convolutional structure attached to its end. It eliminates the need for RPN in faster R-CNN thereby increasing the detection speed drastically [31]. Instead of RPN, it uses multi-scale features and default boxes to make its prediction accuracy be in par with the average accuracy obtained by faster R-CNN [32].…”

Section: Model Constructionmentioning

confidence: 99%

Real-time human detection for electricity conservation using pruned-SSD and arduino

Ushasukhanya

Jothilakshmi

2021

IJECE

View full text Add to dashboard Cite

Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a Modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame.

show abstract

Computer-aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single-Shot Detectors

Cited by 91 publications

References 13 publications

Deep Neural Network for Differentiation of Brain Tumor Tissue Displayed by Confocal Laser Endomicroscopy

Deep Neural Network for Differentiation of Brain Tumor Tissue Displayed by Confocal Laser Endomicroscopy

Current applications of deep-learning in neuro-oncological MRI

Real-time human detection for electricity conservation using pruned-SSD and arduino

Contact Info

Product

Resources

About