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3. HCC Recognition Within B-Mode and CEUS Images Using Traditional and Deep Learning Techniques

Author

Mitrea, Delia, Mendoiu, Cosmina, Mitrea, Paulina, Nedevschi, Sergiu, Lupşor-Platon, Monica, Rotaru, Magda, Badea, Radu, Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Vlad, Simona, editor, and Roman, Nicolae Marius, editor

Published
2022
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4. Towards Building a Computerized System for Modelling Advanced HCC Tumors, in Order to Assist Their Minimum Invasive Surgical Treatment

Author

Mitrea, Delia, Marita, Tiberiu, Vancea, Flaviu, Nedevschi, Sergiu, Mitrea, Paulina, Neamt, Gabriel Mihai, Timoftei, Sanda, Florian, Vlad, Radu, Corina, Socaciu, Mihai, Stefanescu, Horia, Hajjar, Nadim Al, Ceccarelli, Marco, Series Editor, Hernandez, Alfonso, Editorial Board Member, Huang, Tian, Editorial Board Member, Takeda, Yukio, Editorial Board Member, Corves, Burkhard, Editorial Board Member, Agrawal, Sunil, Editorial Board Member, Pisla, Doina, editor, and Vaida, Calin, editor

Published
2020
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6. Towards Building a Computerized System for Modelling Advanced HCC Tumors, in Order to Assist Their Minimum Invasive Surgical Treatment

Author

Mitrea, Delia, primary, Marita, Tiberiu, additional, Vancea, Flaviu, additional, Nedevschi, Sergiu, additional, Mitrea, Paulina, additional, Neamt, Gabriel Mihai, additional, Timoftei, Sanda, additional, Florian, Vlad, additional, Radu, Corina, additional, Socaciu, Mihai, additional, Stefanescu, Horia, additional, and Hajjar, Nadim Al, additional

Published
2020
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11. Modelling Cutaneous Senescence Process

Author

Crisan, Maria, Cattani, Carlo, Badea, Radu, Mitrea, Paulina, Florea, Mira, Crisan, Diana, Mitrea, Delia, Bucur, Razvan, Checiches, Gabriela, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Taniar, David, editor, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Pardede, Eric, editor, and Apduhan, Bernady O., editor

Published
2010
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13. Adversarial Graph Learning and Deep Learning Techniques for improving diagnosis within CT and Ultrasound images

Author

Badea Radu, Mitrea Delia-Alexandrina, Nedevschi Sergiu, and Bacea Dan-Sebastian

Subjects
Treatment response, medicine.diagnostic_test, Coronavirus disease 2019 (COVID-19), business.industry, Computer science, Deep learning, Ultrasound, Pattern recognition, Computed tomography, 02 engineering and technology, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Binary classification, 0202 electrical engineering, electronic engineering, information engineering, medicine, Graph (abstract data type), 020201 artificial intelligence & image processing, In patient, Artificial intelligence, business
Abstract

Computed tomography (CT) examination has an important role in diagnosing, monitoring, and evaluating treatment response in patients infected with COVID-19. Chest CT images provide a reliable alternative to the reverse transcription polymerase chain reaction (RT-PCR). Ultrasonography provides a non-invasive and low-cost solution for diagnosing liver tumors. Recognition of hepatocellular carcinoma (HCC) in ultrasound images can provide a suitable alternative to the more invasive and dangerous method of doing a biopsy. Significant effort has been dedicated to the development of automated ways of diagnosing COVID-19 based on CT scans and on diagnosing liver tumors based on ultrasound images, some of them use deep learning methods. This paper proposes a method for training deep neural networks for performing binary classification, which is called Adversarial Graph Learning. Improved classification performance and reliability is achieved by using our method for both target tasks, recognizing COVID-19 in CT images and recognizing HCC in ultrasound images.

Published
2020

14. Concept mapping, an effective tool for long-term memorization of anatomy—a quasi-experimental research carried out among 1st year general medicine students

Author

Nicoara, Sergiu-Mihai, Szamoskozi, Stefan-Emeric, Mitrea, Delia-Alexandrina, Leucuta, Daniel-Corneliu, Nicoara, Sergiu-Mihai, Szamoskozi, Stefan-Emeric, Mitrea, Delia-Alexandrina, and Leucuta, Daniel-Corneliu

Abstract

This study is part of a doctoral thesis conducted at the Faculty of Psychology of Babes-Bolyai University in collaboration with the University of Medicine, both from Cluj-Napoca, Romania. The starting point of the study was based on the eternal question of the medical student—“How should I learn to manage to retain so much information?” This is how learning through conceptual maps and learning by understanding has been achieved. In the study, a number of 505 students from the Faculty of General Medicine were randomly selected and divided into groups, to observe changes in the grades they obtained when learning anatomy with the concept mapping method vs. traditional methods. Six months later, a retest was carried out to test long-term memory. The results were always in favor of the experimental group and were statistically significant (with one exception), most notably for the 6-month retesting. It was also observed that the language of teaching, different or the same as the first language, explains that exception, at least partially. Other results were taken into account, such as the distribution of bad and good grades in the two groups. Other parameters that influenced the obtained results and which explain some contradictory results in the literature are discussed. In conclusion, the use of conceptual maps is useful for most students, both for short and long-term memory.

Published
2020

15. Integration of Real-Time Image Fusion in the Robotic-Assisted Treatment of Hepatocellular Carcinoma

Author

Radu, Corina, primary, Fisher, Petra, additional, Mitrea, Delia, additional, Birlescu, Iosif, additional, Marita, Tiberiu, additional, Vancea, Flaviu, additional, Florian, Vlad, additional, Tefas, Cristian, additional, Badea, Radu, additional, Ștefănescu, Horia, additional, Nedevschi, Sergiu, additional, Pisla, Doina, additional, and Hajjar, Nadim Al, additional

Published
2020
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17. Modelling Cutaneous Senescence Process

Author

Crisan, Maria, primary, Cattani, Carlo, additional, Badea, Radu, additional, Mitrea, Paulina, additional, Florea, Mira, additional, Crisan, Diana, additional, Mitrea, Delia, additional, Bucur, Razvan, additional, and Checiches, Gabriela, additional

Published
2010
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29. The role of the complex textural microstructure co-occurrence matrices, based on Laws’ features, in the characterization and recognition of some pathological structures, from ultrasound images

Author

Delia Mitrea, Technical University of Cluj-Napoca, Sergiu Nedevschi, Technical University of Cluj-Napoca, Mihail Abrudean, Technical University of Cluj-Napoca, Radu Chifor, Univ. of Medicine and Pharmacy, Cluj-Napoca, Radu Badea, Univ. of Medicine, Cluj, Mitrea, Delia Alexandrina, Nedevschi, Sergiu, Abrudean, Mihail, Chifor, Radu, Badea, Radu, Delia Mitrea, Technical University of Cluj-Napoca, Sergiu Nedevschi, Technical University of Cluj-Napoca, Mihail Abrudean, Technical University of Cluj-Napoca, Radu Chifor, Univ. of Medicine and Pharmacy, Cluj-Napoca, Radu Badea, Univ. of Medicine, Cluj, Mitrea, Delia Alexandrina, Nedevschi, Sergiu, Abrudean, Mihail, Chifor, Radu, and Badea, Radu

Abstract

The non-invasive diagnosis, based on ultrasound images, is a challenge in nowadays research. We develop computerized, texture-based methods, for automatic and computer assisted diagnosis, using the information obtained from ultrasound images. In this work, we defined the co-occurrence matrix of complex textural microstructures determined by using the Laws’ convolution filters and we experimented it in order to perform the characterization and recognition of some important anatomical and pathological structures, within ultrasound images. These structures were the colorectal tumors and the gingival sulcus, the properties of the latter being important concerning the diagnosis and monitoring of the periodontal disease. We determined the textural model of these structures, using the classical and the newly defined textural features. For the automatic recognition, we used powerful classifiers, such as the Multilayer Perceptron, the Support-Vector Machines, decision-trees based classifiers such as Random Forest and C4.5, respectively AdaBoost in combination with the C4.5 algorithm.

Published
2016

39. Software system for the automatic and computer assisted diagnosis of some severe abdominal affections, based on ultrasound images.

Author

Mitrea, Delia, Nedevschi, Sergiu, and Badea, Radu

Subjects
COMPUTERS, MEDICAL care, PATHOLOGY, MEDICALIZATION, PROGNOSIS
Abstract

In this article we describe specific methods for the characterization and computer assisted diagnosis of some severe abdominal diseases, based on ultrasound images and the corresponding software system. The objective of our research is to build an instrument for the non-invasive diagnosis, based on virtual biopsy, of some severe abdominal diseases such as the cirrhosis, the hepatocellular carcinoma (HCC) and the colorectal tumors. The gold standard for the diagnosis of these affections is the classical biopsy, but this is invasive, dangerous. Ultrasonography is a medical-imaging based investigation method that is noninvasive, inexpensive, safe and thus repeatable. Texture is an important property of the human body tissues, able to reveal subtle characteristics upon the pathology. We defined the imagistic textural model of these affections, using the information obtained from ultrasound images. Based on the imagistic textural model, we assessed the possibility of performing automatic recognition of the considered affections both in supervised and unsupervised manner, the purpose of the latter approach being that of discovering the severity grades of the diseases. The resulted accuracy was above 90% in most of the cases. [ABSTRACT FROM AUTHOR]

Published
2016

49. Detecting the Evolution Phases of Hepatocellular Carcinoma from Ultrasound Images, Using Generalized Co-Occurrence Matrices.

Author

Mitrea, Delia, Nedevschi, Sergiu, Abrudean, Mihail, and Badea, Radu

Subjects
LIVER cancer, LIVER tumors, BIOLOGICAL evolution, MULTILAYER perceptrons, FEEDFORWARD neural networks
Abstract

The hepatocellular carcinoma (HCC) is the most frequent malignant liver tumor. Detecting the HCC evolution phases represents an important research challenge. Our aim is to discover the evolution stages of the HCC tumor, through unsupervised classification techniques, using texture-based image analysis methods. We defined the textural model of these phases, consisting of the most relevant textural features for their characterization and of the specific values of these features: arithmetic mean, standard deviation and probability distribution. The role of the features derived from generalized co-occurrence matrices was emphasized in this work. The obtained results were validated through supervised classification methods, such as Multilayer Perceptron (MLP), Support Vector Machines (SVM), decision trees, respectively multi-class meta-classifiers, in combination with the basic learners, yielding classification accuracy above 95%. The method of Self Organizing Maps (SOM) was also employed for data representation. [ABSTRACT FROM AUTHOR]

Published
2015

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