Development of Novel Computer-aided Diagnosis Systems

新型计算机辅助诊断系统的开发

基本信息

  • 批准号:
    RGPIN-2014-05215
  • 负责人:
  • 金额:
    $ 1.82万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2014
  • 资助国家:
    加拿大
  • 起止时间:
    2014-01-01 至 2015-12-31
  • 项目状态:
    已结题

项目摘要

A phenomenal growth in the use of digital images has been observed in recent times in medical diagnosis by physicians. Picture archiving and communications system (PACS) are being widely used in health care as it offers several advantages such as low cost, improved quality, and flexibility in sharing patient data among the health care professionals. It also opens up the possibility of using computers to speed up diagnosis and reduce subjectiveness. The computer-aided diagnosis (CAD) system are already being used for breast cancer screening in many countries, and is expected to be extended for diagnosis of many other kinds of diseases. The image analysis techniques play a key role in developing a CAD system. However, the characteristics of images vary significantly across different modalities (e.g., X-ray, CT, MRI, histopathology), tissue/organ types, and diseases. It is a huge challenge to develop image analysis techniques for such a wide variety of images. In this proposal, we propose to carry on research in two areas: Computer aided diagnosis of Skin Cancer and Tuberculosis (TB). The skin cancer, especially Melanoma, and drug-resistant TB ared deadly diseases with high mortality rate. Early screening of these diseases through computer-aided diagnosis would be very useful in health care. In skin cancer, the proposed research will explore development of histopathological image analysis techniques to improve the diagnosis of Melanoma and Basal cell (the most frequent type of skin cancer) cancer. Techniques would be developed for analyzing both epidermis and dermis areas in a histopathological image. Techniques will also be developed for detecting clusters of nuclei, which are observed in the junction of epidermis and dermis area. Due to the noisy nature of the images, the performance of existing mitosis detection technique is low, and we would explore developing a robust mitosis detection technique based on the sparse representation and classification technique. Appropriate techniques for 3D whole slide histopathological image reconstruction and visualization will be developed to enable the pathologist to see a slide in 3D. In TB diagnosis, robust techniques would be developed for more accurate diagnosis. In addition to cavity, and acinar shadows, the "volume loss" is a hallmark of TB as a result of its destructive and fibrotic nature. The Hilar and mediastinal lymphadenopathy are good indicators of HIV-related TB, and primary TB. Techniques will be developed to detect these indicators, and reduce the false positive rate of typical primary and postprimary TB. Experiments will be conducted with larger databases and with a wider variety of images such as images with acinar shadows, atypical or normal radiographs without acinar shadows, and radiographs with nodules but without acinar shadows. The proposed research will establish a detailed understanding of several important issues related to computer-aided diagnosis of skin cancer, and TB. To our knowledge, we are the first group working on developing a computer-aided diagnosis system for melanoma. We are also one of very few groups who are working on development of efficient computer-aided diagnosis system for TB. A successful development is expected to reduce the diagnosis time and error, and will result in intellectual property.
近年来,在医生的医学诊断中,已经观察到数字图像的使用的显著增长。图像存档和通信系统(PACS)正被广泛应用于医疗保健,因为它提供了几个优点,如低成本,提高质量,并在医疗保健专业人员之间共享患者数据的灵活性。它还开辟了利用计算机加快诊断速度和减少主观性的可能性。计算机辅助诊断(CAD)系统已经在许多国家用于乳腺癌筛查,并有望扩展到许多其他类型疾病的诊断。图像分析技术在CAD系统的开发中起着关键的作用。然而,图像的特性在不同模态之间显著变化(例如,X射线、CT、MRI、组织病理学)、组织/器官类型和疾病。这是一个巨大的挑战,开发图像分析技术,为这样的各种各样的图像。在这个建议中,我们建议在两个领域进行研究:皮肤癌和结核病(TB)的计算机辅助诊断。皮肤癌尤其是黑色素瘤和耐药结核病是致死率高的疾病。通过计算机辅助诊断对这些疾病进行早期筛查将在卫生保健方面非常有用。在皮肤癌方面,拟议的研究将探索组织病理学图像分析技术的发展,以改善黑色素瘤和基底细胞(最常见的皮肤癌类型)癌症的诊断。将开发用于分析组织病理学图像中的表皮和真皮区域的技术。还将开发用于检测细胞核簇的技术,这些细胞核簇在表皮和真皮区域的交界处观察到。由于图像的噪声性质,现有的有丝分裂检测技术的性能较低,我们将探索开发一种鲁棒的有丝分裂检测技术的基础上稀疏表示和分类技术。将开发用于3D全载玻片组织病理学图像重建和可视化的适当技术,以使病理学家能够看到3D载玻片。在结核病诊断方面,将开发可靠的技术以实现更准确的诊断。除了腔和腺泡阴影之外,由于其破坏性和纤维化性质,“体积损失”是TB的标志。肺门和纵隔淋巴结肿大是HIV相关结核和原发性结核的良好指标。将开发检测这些指标的技术,并降低典型的原发性和原发后结核病的假阳性率。将使用更大的数据库和更广泛的图像进行实验,例如具有腺泡阴影的图像、不具有腺泡阴影的非典型或正常X线照片以及具有结节但不具有腺泡阴影的X线照片。这项拟议中的研究将建立对与皮肤癌和结核病的计算机辅助诊断相关的几个重要问题的详细了解。据我们所知,我们是第一个致力于开发黑色素瘤计算机辅助诊断系统的团队。我们也是少数几个致力于开发有效的结核病计算机辅助诊断系统的团体之一。一个成功的发展,预计将减少诊断时间和错误,并将导致知识产权。

项目成果

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Mandal, Mrinal其他文献

Automated analysis and diagnosis of skin melanoma on whole slide histopathological images
全玻片组织病理学图像上皮肤黑色素瘤的自动分析和诊断
  • DOI:
    10.1016/j.patcog.2015.02.023
  • 发表时间:
    2015-08-01
  • 期刊:
  • 影响因子:
    8
  • 作者:
    Lu, Cheng;Mandal, Mrinal
  • 通讯作者:
    Mandal, Mrinal
Automated proliferation index calculation for skin melanoma biopsy images using machine learning
  • DOI:
    10.1016/j.compmedimag.2021.101893
  • 发表时间:
    2021-03-19
  • 期刊:
  • 影响因子:
    5.7
  • 作者:
    Alheejawi, Salah;Berendt, Richard;Mandal, Mrinal
  • 通讯作者:
    Mandal, Mrinal
Pulmonary Thromboendarterectomy Without Circulatory Arrest.
  • DOI:
    10.21470/1678-9741-2020-0534
  • 发表时间:
    2022-05-23
  • 期刊:
  • 影响因子:
    1.3
  • 作者:
    Kynta, Reuben Lamiaki;Rawat, Sanjib;Mandal, Mrinal;Saikia, Manuj Kumar
  • 通讯作者:
    Saikia, Manuj Kumar
Automated image analysis of nuclear atypia in high-power field histopathological image
高倍场组织病理学图像中核异型性的自动图像分析
  • DOI:
    10.1111/jmi.12237
  • 发表时间:
    2015-06-01
  • 期刊:
  • 影响因子:
    2
  • 作者:
    Lu, Cheng;Ji, Mengyao;Mandal, Mrinal
  • 通讯作者:
    Mandal, Mrinal
Solvent H-bond accepting ability induced conformational change and its influence towards fluorescence enhancement and dual fluorescence of hydroxy meta-GFP chromophore analogue
  • DOI:
    10.1039/c6cp04219h
  • 发表时间:
    2016-01-01
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    Chatterjee, Tanmay;Mandal, Mrinal;Mandal, Prasun K.
  • 通讯作者:
    Mandal, Prasun K.

Mandal, Mrinal的其他文献

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{{ truncateString('Mandal, Mrinal', 18)}}的其他基金

Image Analysis and Machine Learning Techniques for Computer-aided Diagnosis
用于计算机辅助诊断的图像分析和机器学习技术
  • 批准号:
    RGPIN-2020-05873
  • 财政年份:
    2022
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Image Analysis and Machine Learning Techniques for Computer-aided Diagnosis
用于计算机辅助诊断的图像分析和机器学习技术
  • 批准号:
    RGPIN-2020-05873
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Image Analysis and Machine Learning Techniques for Computer-aided Diagnosis
用于计算机辅助诊断的图像分析和机器学习技术
  • 批准号:
    RGPIN-2020-05873
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Utilizing image analysis to identify path in closed structures
利用图像分析来识别封闭结构中的路径
  • 批准号:
    543740-2019
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Engage Grants Program
Development of Novel Computer-aided Diagnosis Systems
新型计算机辅助诊断系统的开发
  • 批准号:
    RGPIN-2014-05215
  • 财政年份:
    2018
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Development of Novel Computer-aided Diagnosis Systems
新型计算机辅助诊断系统的开发
  • 批准号:
    RGPIN-2014-05215
  • 财政年份:
    2017
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Development of Novel Computer-aided Diagnosis Systems
新型计算机辅助诊断系统的开发
  • 批准号:
    RGPIN-2014-05215
  • 财政年份:
    2016
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Development of Novel Computer-aided Diagnosis Systems
新型计算机辅助诊断系统的开发
  • 批准号:
    RGPIN-2014-05215
  • 财政年份:
    2015
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Interaction with MetaOptima for exploring skin cancer diagnosis device development
与MetaOptima互动探索皮肤癌诊断设备开发
  • 批准号:
    453422-2013
  • 财政年份:
    2013
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Interaction Grants Program
Intelligent image analysis for multimedia and medical applications
适用于多媒体和医疗应用的智能图像分析
  • 批准号:
    227709-2009
  • 财政年份:
    2013
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual

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