• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5259-5264
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• 2014

Background: CT based brachytherapy allows 3-dimensional (3D) assessment of organs at risk (OAR) doses with dose volume histograms (DVHs). The purpose of this study was to compare computed tomography (CT) based volumetric calculations and International Commission on Radiation Units and Measurements (ICRU) reference-point estimates of radiation doses to the bladder and rectum in patients with carcinoma of the cervix treated with high-dose-rate (HDR) intracavitary brachytherapy (ICBT). Materials and Methods: Between March 2011 and May 2012, 20 patients were treated with 55 fractions of brachytherapy using tandem and ovoids and underwent post-implant CT scans. The external beam radiotherapy (EBRT) dose was 48.6Gy in 27 fractions. HDR brachytherapy was delivered to a dose of 21 Gy in three fractions. The ICRU bladder and rectum point doses along with 4 additional rectal points were recorded. The maximum dose ($D_{Max}$) to rectum was the highest recorded dose at one of these five points. Using the HDRplus 2.6 brachyhtherapy treatment planning system, the bladder and rectum were retrospectively contoured on the 55 CT datasets. The DVHs for rectum and bladder were calculated and the minimum doses to the highest irradiated 2cc area of rectum and bladder were recorded ($D_{2cc}$) for all individual fractions. The mean $D_{2cc}$ of rectum was compared to the means of ICRU rectal point and rectal $D_{Max}$ using the Student's t-test. The mean $D_{2cc}$ of bladder was compared with the mean ICRU bladder point using the same statistical test. The total dose, combining EBRT and HDR brachytherapy, were biologically normalized to the conventional 2 Gy/fraction using the linear-quadratic model. (${\alpha}/{\beta}$ value of 10 Gy for target, 3 Gy for organs at risk). Results: The total prescribed dose was $77.5Gy{\alpha}/{\beta}10$. The mean dose to the rectum was $4.58{\pm}1.22Gy$ for $D_{2cc}$, $3.76{\pm}0.65Gy$ at $D_{ICRU}$ and $4.75{\pm}1.01Gy$ at $D_{Max}$. The mean rectal $D_{2cc}$ dose differed significantly from the mean dose calculated at the ICRU reference point (p<0.005); the mean difference was 0.82 Gy (0.48-1.19Gy). The mean EQD2 was $68.52{\pm}7.24Gy_{{\alpha}/{\beta}3}$ for $D_{2cc}$, $61.71{\pm}2.77Gy_{{\alpha}/{\beta}3}$ at $D_{ICRU}$ and $69.24{\pm}6.02Gy_{{\alpha}/{\beta}3}$ at $D_{Max}$. The mean ratio of $D_{2cc}$ rectum to $D_{ICRU}$ rectum was 1.25 and the mean ratio of $D_{2cc}$ rectum to $D_{Max}$ rectum was 0.98 for all individual fractions. The mean dose to the bladder was $6.00{\pm}1.90Gy$ for $D_{2cc}$ and $5.10{\pm}2.03Gy$ at $D_{ICRU}$. However, the mean $D_{2cc}$ dose did not differ significantly from the mean dose calculated at the ICRU reference point (p=0.307); the mean difference was 0.90 Gy (0.49-1.25Gy). The mean EQD2 was $81.85{\pm}13.03Gy_{{\alpha}/{\beta}3}$ for $D_{2cc}$ and $74.11{\pm}19.39Gy_{{\alpha}/{\beta}3}$ at $D_{ICRU}$. The mean ratio of $D_{2cc}$ bladder to $D_{ICRU}$ bladder was 1.24. In the majority of applications, the maximum dose point was not the ICRU point. On average, the rectum received 77% and bladder received 92% of the prescribed dose. Conclusions: OARs doses assessed by DVH criteria were higher than ICRU point doses. Our data suggest that the estimated dose to the ICRU bladder point may be a reasonable surrogate for the $D_{2cc}$ and rectal $D_{Max}$ for $D_{2cc}$. However, the dose to the ICRU rectal point does not appear to be a reasonable surrogate for the $D_{2cc}$.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.