Mixture-Based Bone Segmentation and Its Application in Computer Aided Diagnosis and Treatment Planning

2005 ◽  
Author(s):  
Hongbing Lu ◽  
Zhengrong Liang ◽  
Bin Li ◽  
Xiang Li ◽  
Jie Meng ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Computer Aided Diagnosis ◽  
Diagnosis And Treatment ◽  
Bone Segmentation ◽  
Computer Aided ◽  
Aided Diagnosis

Orthodontics: computer-aided diagnosis and treatment planning

2000 ◽  
Author(s):  
Yaxing Yi ◽  
Zhongke Li ◽  
Suyuan Wei ◽  
Fanglin Deng ◽  
Sen Yao
Keyword(s):  
Treatment Planning ◽  
Computer Aided Diagnosis ◽  
Diagnosis And Treatment ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation

10.2196/18563 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e18563
Author(s):  
Muhammad Owais ◽  
Muhammad Arsalan ◽  
Tahir Mahmood ◽  
Jin Kyu Kang ◽  
Kang Ryoung Park
Keyword(s):  
Deep Learning ◽  
Medical Condition ◽  
Early Stage ◽  
Gastrointestinal Diseases ◽  
Computer Aided Diagnosis ◽  
Diagnosis And Treatment ◽  
Computer Prediction ◽  
Gastrointestinal Lesions ◽  
Computer Aided ◽  
Aided Diagnosis

Background The early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning–based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract. Objective This study aimed to develop a comprehensive computer-aided diagnosis tool to assist medical experts in diagnosing various types of gastrointestinal diseases. Methods Our proposed framework comprises a deep learning–based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database. These past cases help the medical expert validate the current computer prediction subjectively, which ultimately results in better diagnosis and treatment. Results All the experiments were performed using 2 endoscopic data sets with a total of 52,471 frames and 37 different classes. The optimal performances obtained by our proposed method in accuracy, F1 score, mean average precision, and mean average recall were 96.19%, 96.99%, 98.18%, and 95.86%, respectively. The overall performance of our proposed diagnostic framework substantially outperformed state-of-the-art methods. Conclusions This study provides a comprehensive computer-aided diagnosis framework for identifying various types of gastrointestinal diseases. The results show the superiority of our proposed method over various other recent methods and illustrate its potential for clinical diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences.

scholarly journals Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Muhammad Owais ◽  
Muhammad Arsalan ◽  
Tahir Mahmood ◽  
Jin Kyu Kang ◽  
Kang Ryoung Park
Keyword(s):  
Deep Learning ◽  
Medical Condition ◽  
Early Stage ◽  
Gastrointestinal Diseases ◽  
Computer Aided Diagnosis ◽  
Diagnosis And Treatment ◽  
Computer Prediction ◽  
Gastrointestinal Lesions ◽  
Computer Aided ◽  
Aided Diagnosis

BACKGROUND The early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning–based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract. OBJECTIVE This study aimed to develop a comprehensive computer-aided diagnosis tool to assist medical experts in diagnosing various types of gastrointestinal diseases. METHODS Our proposed framework comprises a deep learning–based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database. These past cases help the medical expert validate the current computer prediction subjectively, which ultimately results in better diagnosis and treatment. RESULTS All the experiments were performed using 2 endoscopic data sets with a total of 52,471 frames and 37 different classes. The optimal performances obtained by our proposed method in accuracy, F1 score, mean average precision, and mean average recall were 96.19%, 96.99%, 98.18%, and 95.86%, respectively. The overall performance of our proposed diagnostic framework substantially outperformed state-of-the-art methods. CONCLUSIONS This study provides a comprehensive computer-aided diagnosis framework for identifying various types of gastrointestinal diseases. The results show the superiority of our proposed method over various other recent methods and illustrate its potential for clinical diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences.

Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis

2018 ◽  
Vol 11 (4) ◽  
pp. 365-374 ◽  
Author(s):  
Hidetaka Arimura ◽  
Mazen Soufi ◽  
Kenta Ninomiya ◽  
Hidemi Kamezawa ◽  
Masahiro Yamada
Keyword(s):  
Cancer Diagnosis ◽  
Computer Aided Diagnosis ◽  
Diagnosis And Treatment ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Hui Wang ◽  
Yanying Li ◽  
Shanshan Liu ◽  
Xianwen Yue
Keyword(s):  
Lung Cancer ◽  
Image Annotation ◽  
Lung Nodule ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Diagnosis And Treatment ◽  
Lung Nodules ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

At present, the diagnosis and treatment of lung cancer have always been one of the research hotspots in the medical field. Early diagnosis and treatment of this disease are necessary means to improve the survival rate of lung cancer patients and reduce their mortality. The introduction of computer-aided diagnosis technology can easily, quickly, and accurately identify the lung nodule area as an imaging feature of early lung cancer for the clinical diagnosis of lung cancer and is helpful for the quantitative analysis of the characteristics of lung nodules and is useful for distinguishing benign and malignant lung nodules. Growth provides an objective diagnostic reference standard. This paper studies ITK and VTK toolkits and builds a system platform with MFC. By studying the process of doctors diagnosing lung nodules, the whole system is divided into seven modules: suspected lung shadow detection, image display and image annotation, and interaction. The system passes through the entire lung nodule auxiliary diagnosis process and obtains the number of nodules, the number of malignant nodules, and the number of false positives in each set of lung CT images to analyze the performance of the auxiliary diagnosis system. In this paper, a lung region segmentation method is proposed, which makes use of the obvious differences between the lung parenchyma and other human tissues connected with it, as well as the position relationship and shape characteristics of each human tissue in the image. Experiments are carried out to solve the problems of lung boundary, inaccurate segmentation of lung wall, and depression caused by noise and pleural nodule adhesion. Experiments show that there are 2316 CT images in 8 sets of images of different patients, and the number of nodules is 56. A total of 49 nodules were detected by the system, 7 were missed, and the detection rate was 87.5%. A total of 64 false-positive nodules were detected, with an average of 8 per set of images. This shows that the system is effective for CT images of different devices, pixel pitch, and slice pitch and has high sensitivity, which can provide doctors with good advice.

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