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Anthony P. Reeves

Other affiliations: Purdue University, Ithaca College, University of Chicago  ...read more
Bio: Anthony P. Reeves is an academic researcher from Cornell University. The author has contributed to research in topics: Nodule (medicine) & Lung cancer. The author has an hindex of 39, co-authored 208 publications receiving 8928 citations. Previous affiliations of Anthony P. Reeves include Purdue University & Ithaca College.


Papers
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Journal ArticleDOI
TL;DR: The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus and is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.
Abstract: Purpose: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories (" nodule�3 mm," " nodule<3 mm," and "non- nodule�3 mm "). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked " nodul�3 mm " by at least one radiologist, of which 928 (34.7) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice. © 2011 U.S. Government.

1,923 citations

Journal ArticleDOI
TL;DR: Basic Cartesian moment theory is reviewed and its application to object recognition and image analysis is presented and the geometric properties of low-order moments are discussed along with the definition of several moment-space linear geometric transforms.

620 citations

Journal ArticleDOI
TL;DR: CT volumetric measurements are highly accurate for determining volume and are useful in assessing growth of small nodules and calculating their doubling times.
Abstract: PURPOSE: To determine the accuracy of high-resolution computed tomographic (CT) volumetric measurements of small pulmonary nodules to assess growth and malignancy status. MATERIALS AND METHODS: The accuracy of three-dimensional (3D) image extraction and isotropic resampling techniques was assessed by performing three experiments. The first experiment measured volumes in spherical synthetic nodules of two diameters (3.20 and 3.96 mm), the second measured deformable silicone synthetic nodules prior to and after their shape had been altered markedly, and the third measured nodules of various shapes and sizes. Three-dimensional techniques were used to assess growth in 13 patients for whom the final diagnosis was known and whose initial nodule diameters were less than 10 mm. By using the exponential growth model and the calculated nodule volume at two points in time, the doubling time for each subject was calculated. RESULTS: The three synthetic nodule studies revealed that the volume could be measured accurat...

574 citations

Journal ArticleDOI
TL;DR: Through a consensus process in which careful planning and proper consideration of fundamental issues have been emphasized, the LIDC database is expected to provide a powerful resource for the medical imaging research community.
Abstract: To stimulate the advancement of computer-aided diagnostic (CAD) research for lung nodules in thoracic computed tomography (CT), the National Cancer Institute launched a cooperative effort known as the Lung Image Database Consortium (LIDC) The LIDC is composed of five academic institutions from across the United States that are working together to develop an image database that will serve as an international research resource for the development, training, and evaluation of CAD methods in the detection of lung nodules on CT scans Prior to the collection of CT images and associated patient data, the LIDC has been engaged in a consensus process to identify, address, and resolve a host of challenging technical and clinical issues to provide a solid foundation for a scientifically robust database These issues include the establishment of (a) a governing mission statement, (b) criteria to determine whether a CT scan is eligible for inclusion in the database, (c) an appropriate definition of the term qualifying nodule, (d) an appropriate definition of "truth" requirements, (e) a process model through which the database will be populated, and (f) a statistical framework to guide the application of assessment methods by users of the database Through a consensus process in which careful planning and proper consideration of fundamental issues have been emphasized, the LIDC database is expected to provide a powerful resource for the medical imaging research community This article is intended to share with the community the breadth and depth of these key issues

386 citations

Patent
10 Apr 2001
TL;DR: In this paper, the authors present methods and systems for conducting three-dimensional image analysis and diagnosis and possible treatment relating thereto, which includes methods of handling signals containing information (data) relating to 3D representation of objects scanned by a scanning medium.
Abstract: The present invention relates to methods and systems for conducting three-dimensional image analysis and diagnosis and possible treatment relating thereto. The invention includes methods of handling signals containing information (data) relating to three-dimensional representation of objects scanned by a scanning medium. The invention also includes methods of making and analyzing volumetric measurements and changes in volumetric measurements which can be used for the purpose of diagnosis and treatment.

383 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.

8,730 citations

Journal ArticleDOI
TL;DR: This new adenocarcinoma classification is needed to provide uniform terminology and diagnostic criteria, especially for bronchioloalveolar carcinoma (BAC), the overall approach to small nonresection cancer specimens, and for multidisciplinary strategic management of tissue for molecular and immunohistochemical studies.

3,850 citations

Journal ArticleDOI
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.
Abstract: In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource

3,699 citations

Journal ArticleDOI
TL;DR: PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images, is developed and its application in characterizing lung lesions is demonstrated.
Abstract: Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at www.radiomics.io With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. Cancer Res; 77(21); e104-7. ©2017 AACR.

2,905 citations