scholarly journals Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mahsa Bank Tavakoli ◽  
Mahdi Orooji ◽  
Mehdi Teimouri ◽  
Ramita Shahabifar
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Standards Of Care ◽  
Shape Features ◽  
Selection Methods ◽  
Segmentation Framework ◽  
Vessel Tortuosity ◽  
Benign Nodules ◽  
Degradation Evaluation

Abstract Objective The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuosity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker patients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted. Results We compare our framework with the state-of-the-art feature selection methods for differentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.

scholarly journals Distinguishing Adenocarcinomas From Granulomas in The CT Scan of The Chest: Performance Degradation Evaluation in The Automatic Segmentation Framework

2021 ◽  
Author(s):  
Mahsa Bank Tavakoli ◽  
Mahdi Orooji ◽  
Mehdi Teimouri ◽  
Ramita Shahabifar
Keyword(s):  
Ct Scan ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Texture Features ◽  
The State ◽  
Ct Scans ◽  
Standards Of Care ◽  
Shape Features ◽  
Segmentation Framework ◽  
Degradation Evaluation

Abstract Objective: The most common histopathologic malignant and benign nod- ules are Adenocarcinoma and Granuloma, respectively, which have di_erent standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuos- ity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker pa- tients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted. Results: We compare our framework with the state-of-the-art feature selec- tion methods for di_erentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.

scholarly journals Distinguishing Adenocarcinomas From Granulomas in the CT Scan of the Chest: Performance Degradation Evaluation in the Automatic Segmentation Framework

2020 ◽  
Author(s):  
Mahsa Bank Tavakoli ◽  
Mahdi Orooji ◽  
Mehdi Teimouri ◽  
Ramita Shahabifar
Keyword(s):  
Ct Scan ◽  
Automatic Segmentation ◽  
Pulmonary Nodules ◽  
Texture Features ◽  
Standards Of Care ◽  
Svm Classifier ◽  
Similar Appearance ◽  
Auc Value ◽  
Benign Nodules ◽  
Degradation Evaluation

Abstract Background:The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. However, distinguishing Adenocarcinoma from Granuloma in the CT scan of the chest is a challenging task, due to the similar appearance in shape and appearance. Indeed, biopsies are needed for the diagnosis. The radiomic features of pulmonary nodules, along with the torsion of the vessels attached to the nodules are accepted by expert radiologists as the biomarker for discriminating the benign nodules from the malignant ones. In this paper, we propose an automatic framework for the distinction of the Adenocarcinomas and the Granulomas in CTs using the radiomic features of nodules and the attached vessel tortuosity.Methods:To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature mean and the number of the attached vessels are extracted. Then, we apply a trained SVM classifier to identify the segmented nodule as the Adenocarcinoma or the Granuloma.Results:The proposed framework is evaluated on a private dataset, including 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma (44 CTs for both nodule types). The dataset contains the CTs of the non-smoker patients who are between 30 and 60 years old. Compared to the state-of-the-art feature selection methods which employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule for differentiating Adenocarcinomas from Granulomas, the accuracy of our framework is improved by 21:39%, 3:72%, and 8:27%, respectively. The area under the ROC curve of the introduced framework for the manually and automatically segmented nodules is 0:8874 and 0:7583, respectively.Conclusions:The AUC value for the automatically segmented nodules is lower than that of the manual ones labeled by a radiologist. However, the time run of the introduced framework for the automatically segmented nodules is much lower than that of the manual ones.

scholarly journals Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

2021 ◽  
Author(s):  
Jorge F. Lazo ◽  
Aldo Marzullo ◽  
Sara Moccia ◽  
Michele Catellani ◽  
Benoit Rosa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Temporal Information ◽  
Invasive Technique ◽  
Dice Similarity Coefficient ◽  
Specular Reflections ◽  
Lumen Segmentation ◽  
Previous State

Abstract Purpose Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma. During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on convolutional neural networks (CNNs). Methods The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks ($$m_1$$ m 1 ) and Mask-RCNN ($$m_2$$ m 2 ), which are fed with single still-frames I(t). The other two models ($$M_1$$ M 1 , $$M_2$$ M 2 ) are modifications of the former ones consisting on the addition of a stage which makes use of 3D convolutions to process temporal information. $$M_1$$ M 1 , $$M_2$$ M 2 are fed with triplets of frames ($$I(t-1)$$ I ( t - 1 ) , I(t), $$I(t+1)$$ I ( t + 1 ) ) to produce the segmentation for I(t). Results The proposed method was evaluated using a custom dataset of 11 videos (2673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in the presence of poor visibility, occasional bleeding, or specular reflections.

Selected Shape and Texture Features for Automatic Detection of Acute Lymphoblastic Leukemia

2018 ◽  
pp. 162-183
Author(s):  
Vanika Singhal ◽  
Preety Singh
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
Principal Component ◽  
Texture Features ◽  
Automatic Detection ◽  
Support Vector ◽  
Shape Features ◽  
Geometrical Properties ◽  
Correlation Based Feature Selection ◽  
Human Interpretation

Acute Lymphoblastic Leukemia is a cancer of blood caused due to increase in number of immature lymphocyte cells. Detection is done manually by skilled pathologists which is time consuming and depends on the skills of the pathologist. The authors propose a methodology for discrimination of a normal lymphocyte cell from a malignant one by processing the blood sample image. Automatic detection process will reduce the diagnosis time and not be limited by human interpretation. The lymphocyte images are classified based on two types of extracted features: shape and texture. To identify prominent shape features, Correlation based Feature Selection is applied. Principal Component Analysis is applied on the texture features to reduce their dimensionality. Support Vector Machine is used for classification. It is observed that 16 shape features are able to give a classification accuracy of 92.3% and that changes in the geometrical properties of the nucleus emerge as significant features contributing towards detecting a malignant lymphocyte.

The Art of Shaping Materials

2019 ◽  
Vol 8 (3-4) ◽  
pp. 407-433 ◽  
Author(s):  
Filipp Schmidt
Keyword(s):  
Visual Perception ◽  
Human Skin ◽  
Material Properties ◽  
Vision Research ◽  
Texture Features ◽  
Viscous Fluids ◽  
Shape Features ◽  
Visual Artists ◽  
Material Perception ◽  
Single Block

Material perception — the visual perception of stuff — is an emerging field in vision research. We recognize materials from shape, color and texture features. This paper is a selective review and discussion of how artists have been using shape features to evoke vivid impressions of specific materials and material properties. A number of examples are presented in which visual artists render materials or their transformations, such as soft human skin, runny or viscous fluids, or wrinkled cloth. They achieve this by expressing the telltale shape features of these materials and transformations, often by carving them from a single block of marble or wood. Vision research has just begun to investigate these very shape features, making material perception a prime example of how art can inform science.

scholarly journals Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features

2020 ◽  
Vol 10 (14) ◽  
pp. 4791 ◽  
Author(s):  
Pedro Narváez ◽  
Steven Gutierrez ◽  
Winston S. Percybrooks
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Automatic Classification ◽  
The State ◽  
Heart Sounds ◽  
Empirical Wavelet Transform ◽  
Art Methods

A system for the automatic classification of cardiac sounds can be of great help for doctors in the diagnosis of cardiac diseases. Generally speaking, the main stages of such systems are (i) the pre-processing of the heart sound signal, (ii) the segmentation of the cardiac cycles, (iii) feature extraction and (iv) classification. In this paper, we propose methods for each of these stages. The modified empirical wavelet transform (EWT) and the normalized Shannon average energy are used in pre-processing and automatic segmentation to identify the systolic and diastolic intervals in a heart sound recording; then, six power characteristics are extracted (three for the systole and three for the diastole)—the motivation behind using power features is to achieve a low computational cost to facilitate eventual real-time implementations. Finally, different models of machine learning (support vector machine (SVM), k-nearest neighbor (KNN), random forest and multilayer perceptron) are used to determine the classifier with the best performance. The automatic segmentation method was tested with the heart sounds from the Pascal Challenge database. The results indicated an error (computed as the sum of the differences between manual segmentation labels from the database and the segmentation labels obtained by the proposed algorithm) of 843,440.8 for dataset A and 17,074.1 for dataset B, which are better values than those reported with the state-of-the-art methods. For automatic classification, 805 sample recordings from different databases were used. The best accuracy result was 99.26% using the KNN classifier, with a specificity of 100% and a sensitivity of 98.57%. These results compare favorably with similar works using the state-of-the-art methods.

A New Object Recognition Approach Using Combination of Texture, Color and Shape Features

2015 ◽  
Vol 761 ◽  
pp. 111-115
Author(s):  
Abdul Kadir ◽  
K.A.A. Aziz ◽  
Irianto
Keyword(s):  
Texture Features ◽  
Shape Features ◽  
Accuracy Rate ◽  
New Approach ◽  
Orthogonal Moments ◽  
Krawtchouk Moments ◽  
Image Histogram ◽  
Color Features ◽  
Tchebichef Moments ◽  
Skewness And Kurtosis

This paper reports a new approach for recognizing objects by using combination of texture, color and shape features. Texture features were generated by applying statistical calculation on the image histogram. Color features were computed by using mean, standard deviation, skewness and kurtosis. Shape features were generated using combination of Shen features and basic shapes such as eccentricity and dispersion. The total features were used much less compared to approaches that involve orthogonal moments such as Krawtchouk moments, Zernike moments, or Tchebichef moments. Testing was done by using a dataset that contains 53 kinds of objects. All objects contained in the dataset were various things that can be found in supermarkets or produced by manufacturing. The result shows that the system gave 98.11% of accuracy rate.

scholarly journals An Evaluation of Atlas Selection Methods for Atlas-Based Automatic Segmentation in Radiotherapy Treatment Planning

2019 ◽  
Vol 38 (11) ◽  
pp. 2654-2664 ◽  
Author(s):  
Bas Schipaanboord ◽  
Djamal Boukerroui ◽  
Devis Peressutti ◽  
Johan van Soest ◽  
Tim Lustberg ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Automatic Segmentation ◽  
Selection Methods ◽  
Radiotherapy Treatment Planning ◽  
Radiotherapy Treatment

scholarly journals Aplastic anemia: immunosuppressive therapy in 2010

2011 ◽  
Vol 3 (2s) ◽  
pp. 7 ◽  
Author(s):  
Antonio M. Risitano ◽  
Fabiana Perna
Keyword(s):  
Bone Marrow ◽  
Aplastic Anemia ◽  
State Of The Art ◽  
Bone Marrow Failure ◽  
Standards Of Care ◽  
Bone Marrow Failure Syndrome ◽  
Clinical Observations ◽  
Immune Mediated ◽  
Experimental Works ◽  
Current Standards

Acquired aplastic anemia (AA) is the typical bone marrow failure syndrome characterized by an empty bone marrow; an immune-mediated pathophysiology has been demonstrated by experimental works as well as by clinical observations. Immunusuppressive therapy (IST) is a key treatment strategy for aplastic anemia; since 20 years the standard IST for AA patients has been anti-thymocyte globuline (ATG) plus cyclosporine A (CyA), which results in response rates ranging between 50% and 70%, and even higher overall survival. However, primary and secondary failures after IST remain frequent, and to date all attempts aiming to overcome this problem have been unfruitful. Here we review the state of the art of IST for AA in 2010, focusing on possible strategies to improve current treatments. We also discuss very recent data which question the equality of different ATG preparations, leading to a possible reconsideration of the current standards of care for AA patients.

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