scholarly journals Automatic Segmentation of Prostate Cancer using cascaded Fully Convolutional Network

2021 ◽  
Vol 309 ◽  
pp. 01068
Author(s):  
Padmavathi Kora ◽  
K Reddy Madhavi ◽  
J Avanija ◽  
Sunitha Gurram ◽  
K Meenakshi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Neural Networks ◽  
Automatic Segmentation ◽  
Learning Method ◽  
Extraction Techniques ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Tumour Detection ◽  
Prostate Segmentation ◽  
Learning Techniques

In this paper we proposed a prostate segmentation and also tumour detection using deep neural networks. The cutting-edge deep learning techniques are useful compared to the challenges of machine learning based feature extraction techniques. Here we proposed a strategy that contains an FCN model that incorporates data from several MRI images, allowing for faster convergence and more accurate segmentation. T1 and DWI volumes may be used together to delineate the prostate boundary, according to this study. Second, we investigated whether this method might be utilized to provide voxel-level prostate tumor forecasts. The cascaded learning method and performed tests to demonstrate its effectiveness.

Predicting Spatiotemporal Demand of Dockless E-Scooter Sharing Services with a Masked Fully Convolutional Network

2021 ◽  
Vol 10 (11) ◽  
pp. 773
Author(s):  
Santi Phithakkitnukooon ◽  
Karn Patanukhom ◽  
Merkebe Getachew Demissie
Keyword(s):  
Loss Function ◽  
Short Term Memory ◽  
Selection Process ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Demand Prediction ◽  
Learning Techniques ◽  
Last Mile ◽  
Long Short Term Memory ◽  
Highly Correlated

Dockless electric scooters (e-scooter) have emerged as a green alternative to automobiles and a solution to the first- and last-mile problems. Demand anticipation, or being able to accurately predict spatiotemporal demand of e-scooter usage, is one supply–demand balancing strategy. In this paper, we present a dockless e-scooter demand prediction model based on a fully convolutional network (FCN) coupled with a masking process and a weighted loss function, namely, masked FCN (or MFCN). The MFCN model handles the sparse e-scooter usage data with its masking process and weighted loss function. The model is trained with highly correlated features through our feature selection process. Next-hour and next 24-h prediction schemes have been tested for both pick-up and drop-off demands. Overall, the proposed MFCN outperforms other baseline models including a naïve forecasting, linear regression, and convolutional long short-term memory networks with mean absolute errors of 0.0434 and 0.0464 for the next-hour pick-up and drop-off demand prediction, respectively, and the errors of 0.0491 and 0.0501 for the next 24-h pick-up and drop-off demand prediction, respectively. The developed MFCN expands the collection of deep learning techniques that can be applied in the transportation domain, especially spatiotemporal demand prediction.

scholarly journals Time Series Classification with InceptionFCN

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 157
Author(s):  
Saidrasul Usmankhujaev ◽  
Bunyodbek Ibrokhimov ◽  
Shokhrukh Baydadaev ◽  
Jangwoo Kwon
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Learning Approaches ◽  
Time Series Classification ◽  
Convolutional Network ◽  
Training Time ◽  
Learning Techniques ◽  
Significant Research ◽  
Previous State

Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last decade, and significant research has been proposed with various solutions, including algorithm-based approaches as well as machine and deep learning approaches. This paper focuses on combining the two well-known deep learning techniques, namely the Inception module and the Fully Convolutional Network. The proposed method proved to be more efficient than the previous state-of-the-art InceptionTime method. We tested our model on the univariate TSC benchmark (the UCR/UEA archive), which includes 85 time-series datasets, and proved that our network outperforms the InceptionTime in terms of the training time and overall accuracy on the UCR archive.

scholarly journals A Review on Prostate Cancer Detection using Deep Learning Techniques

2020 ◽  
Vol 1 (2) ◽  
pp. 26-33
Author(s):  
Narmatha C ◽  
◽  
Surendra Prasad M ◽  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Medical Image Analysis ◽  
Diagnostic Methods ◽  
Convolutional Networks ◽  
Prostate Segmentation ◽  
Learning Techniques ◽  
Imaging Methodology ◽  
Medical Effect

The second most diagnosed disease of men throughout the world is Prostate cancer (PCa). 28% of cancers in men result in the prostate, making PCa and its identification an essential focus in cancer research. Hence, developing effective diagnostic methods for PCa is very significant and has critical medical effect. These methods could improve the advantages of treatment and enhance the patients' survival chance. Imaging plays a significant role in the identification of PCa. Prostate segmentation and classification is a difficult process, and the difficulties fundamentally vary with one imaging methodology then onto the next. For segmentation and classification, deep learning algorithms, specifically convolutional networks, have quickly become an optional technique for medical image analysis. In this survey, various types of imaging modalities utilized for diagnosing PCa is reviewed and researches made on the detection of PCa is analyzed. Most of the researches are done in machine learning based and deep learning based techniques. Based on the results obtained from the analysis of these researches, deep learning based techniques plays a significant and promising part in detecting PCa. Most of the techniques are based on computer aided detection (CAD) systems, which follows preprocessing, segmentation, feature extraction, and classification processes, which yield efficient results in detecting PCa. As a conclusion from the analysis of some recent works, deep learning based techniques are adequate for the detection of PCa.

scholarly journals Fully convolutional network-based multi-output model for automatic segmentation of organs at risk in thorax

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110201
Author(s):  
Jie Zhang ◽  
Yiwei Yang ◽  
Kainan Shao ◽  
Xue Bai ◽  
Min Fang ◽  
...  
Keyword(s):  
Spinal Cord ◽  
At Risk ◽  
Organs At Risk ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Bilateral Lung ◽  
The Impact ◽  
Ct Slices

Purpose: To propose a multi-output fully convolutional network (MOFCN) to segment bilateral lung, heart and spinal cord in the planning thoracic computed tomography (CT) slices automatically and simultaneously. Methods: The MOFCN includes two components: one main backbone and three branches. The main backbone extracts the features about lung, heart and spinal cord. The extracted features are transferred to three branches which correspond to three organs respectively. The longest branch to segment spinal cord is nine layers, including input and output layers. The MOFCN was evaluated on 19,277 CT slices from 966 patients with cancer in the thorax. In these slices, the organs at risk (OARs) were delineated and validated by experienced radiation oncologists, and served as ground truth for training and evaluation. The data from 61 randomly chosen patients were used for training and validation. The remaining 905 patients’ slices were used for testing. The metric used to evaluate the similarity between the auto-segmented organs and their ground truth was Dice. Besides, we compared the MOFCN with other published models. To assess the distinct output design and the impact of layer number and dilated convolution, we compared MOFCN with a multi-label learning model and its variants. By analyzing the not good performances, we suggested possible solutions. Results: MOFCN achieved Dice of 0.95  ±  0.02 for lung, 0.91  ±  0.03 for heart and 0.87  ±  0.06 for spinal cord. Compared to other models, MOFCN could achieve a comparable accuracy with the least time cost. Conclusion: The results demonstrated the MOFCN’s effectiveness. It uses less parameters to delineate three OARs simultaneously and automatically, and thus shows a relatively low requirement for hardware and has potential for broad application.

scholarly journals A Novel Automated Method for COVID-19 Infection and Lung Segmentation using Deep Neural Networks

2020 ◽  
Author(s):  
Shreyas Mishra
Keyword(s):  
Deep Neural Networks ◽  
Cross Validation ◽  
Automatic Segmentation ◽  
Automated Segmentation ◽  
Test Results ◽  
Similarity Coefficients ◽  
Automated Method ◽  
Learning Techniques ◽  
Near Future ◽  
Lung Ct

Abstract The COVID-19 pandemic first originated in Wuhan, China and has spread to every country in the world. Without a viable cure in the near future, there is an urgent need for rapid diagnosis of COVID-19, faster test results and automated segmentation of infected region in the lungs. The aim of this paper is to assist in the rapid detection and segmentation of COVID-19 patients using deep learning techniques. This paper proposes a method for automatic segmentation of the lung and infected regions of COVID 19 patients using lung CT scan dataset. This has been done using a modified U-Net model along with different cross validation folds. The region of infection which is segmented will contain the lesion, which if identified in the early stages can be beneficial during treatment of the person. This can help doctors to determine the severity of the infection and suggest treatments based on it. A comparative analysis of the proposed architectures has been done against recently published results which proves the superiority of our models in terms of dice similarity coefficients.

scholarly journals Domain adaptation for segmentation of critical structures for prostate cancer therapy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anneke Meyer ◽  
Alireza Mehrtash ◽  
Marko Rak ◽  
Oleksii Bashkanov ◽  
Bjoern Langbein ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Domain Adaptation ◽  
Automatic Segmentation ◽  
Preoperative Assessment ◽  
Patient Specific ◽  
Rank Test ◽  
Target Domain ◽  
Convolutional Network ◽  
Anatomical Model ◽  
Self Learning

AbstractPreoperative assessment of the proximity of critical structures to the tumors is crucial in avoiding unnecessary damage during prostate cancer treatment. A patient-specific 3D anatomical model of those structures, namely the neurovascular bundles (NVB) and the external urethral sphincters (EUS), can enable physicians to perform such assessments intuitively. As a crucial step to generate a patient-specific anatomical model from preoperative MRI in a clinical routine, we propose a multi-class automatic segmentation based on an anisotropic convolutional network. Our specific challenge is to train the network model on a unique source dataset only available at a single clinical site and deploy it to another target site without sharing the original images or labels. As network models trained on data from a single source suffer from quality loss due to the domain shift, we propose a semi-supervised domain adaptation (DA) method to refine the model’s performance in the target domain. Our DA method combines transfer learning and uncertainty guided self-learning based on deep ensembles. Experiments on the segmentation of the prostate, NVB, and EUS, show significant performance gain with the combination of those techniques compared to pure TL and the combination of TL with simple self-learning ($${p}<0.005$$ p < 0.005 for all structures using a Wilcoxon’s signed-rank test). Results on a different task and data (Pancreas CT segmentation) demonstrate our method’s generic application capabilities. Our method has the advantage that it does not require any further data from the source domain, unlike the majority of recent domain adaptation strategies. This makes our method suitable for clinical applications, where the sharing of patient data is restricted.

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