SPIE Proceedings

2012

DOI: 10.1117/12.911700

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Detection of sclerotic bone metastases in the spine using watershed algorithm and graph cut

Tatjana Wiese¹,

Joseph E. Burns³

et al.

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Introduction3

Fracture Detection1

Bone Oncology1

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Cited by 18 publications

(16 citation statements)

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“…Thorough manual assessment and processing is timeconsuming and often delays the clinical workflow. Therefore CADe has the potential to greatly reduce the radiologists' clinical workload and to serve as a first or second reader for improved assessment of the disease [3], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation

¹

,

²

,

³

et al. 2016

IEEE Trans. Med. Imaging

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Automated computer-aided detection (CADe) has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-to-fine cascade framework that first operates a candidate generation system at sensitivities ∼ 100% of but at high FP levels. By leveraging existing CADe systems, coordinates of regions or volumes of interest (ROI or VOI) are generated and function as input for a second tier, which is our focus in this study. In this second stage, we generate 2D (two-dimensional) or 2.5D views via sampling through scale transformations, random translations and rotations. These random views are used to train deep convolutional neural network (ConvNet) classifiers. In testing, the ConvNets assign class (e.g., lesion, pathology) probabilities for a new set of random views that are then averaged to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. The methods are evaluated on three data sets: 59 patients for sclerotic metastasis detection, 176 patients for lymph node detection, and 1,186 patients for colonic polyp detection. Experimental results show the ability of ConvNets to generalize well to different medical imaging CADe applications and scale elegantly to various data sets. Our proposed methods improve performance markedly in all cases. Sensitivities improved from 57% to 70%, 43% to 77%, and 58% to 75% at 3 FPs per patient for sclerotic metastases, lymph nodes and colonic polyps, respectively.

“…Thorough manual assessment and processing is timeconsuming and often delays the clinical workflow. Therefore CADe has the potential to greatly reduce the radiologists' clinical workload and to serve as a first or second reader for improved assessment of the disease [3], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation

¹

,

²

,

³

et al. 2016

IEEE Trans. Med. Imaging

Self Cite

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Automated computer-aided detection (CADe) has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-to-fine cascade framework that first operates a candidate generation system at sensitivities ∼ 100% of but at high FP levels. By leveraging existing CADe systems, coordinates of regions or volumes of interest (ROI or VOI) are generated and function as input for a second tier, which is our focus in this study. In this second stage, we generate 2D (two-dimensional) or 2.5D views via sampling through scale transformations, random translations and rotations. These random views are used to train deep convolutional neural network (ConvNet) classifiers. In testing, the ConvNets assign class (e.g., lesion, pathology) probabilities for a new set of random views that are then averaged to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. The methods are evaluated on three data sets: 59 patients for sclerotic metastasis detection, 176 patients for lymph node detection, and 1,186 patients for colonic polyp detection. Experimental results show the ability of ConvNets to generalize well to different medical imaging CADe applications and scale elegantly to various data sets. Our proposed methods improve performance markedly in all cases. Sensitivities improved from 57% to 70%, 43% to 77%, and 58% to 75% at 3 FPs per patient for sclerotic metastases, lymph nodes and colonic polyps, respectively.

“…For example, Roth et al [10] used a deep convolutional neural network (CNN) as the 2nd tier of a two-tiered, coarse-to-fine, cascade framework to refine the candidate lesions from the first tier for sclerotic spine metastasis detection in computer tomography (CT) images. Wiese et al [11] developed an automatic method based on a watershed algorithm and graph cut for detecting sclerotic spine metastases in CT images. And Yao et al [12] applied a support vector machine to refine the initial detections produced with a watershed algorithm for lytic bone metastasis detection in CT images.…”

Section: Introductionmentioning

confidence: 99%

A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks

¹

,

²

,

³

et al. 2017

Computers in Biology and Medicine

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Spinal metastasis, a metastatic cancer of the spine, is the most common malignant disease in the spine. In this study, we investigate the feasibility of automated spinal metastasis detection in magnetic resonance imaging (MRI) by using deep learning methods. To accommodate the large variability in metastatic lesion sizes, we develop a Siamese deep neural network approach comprising three identical subnetworks for multi-resolution analysis and detection of spinal metastasis. At each location of interest, three image patches at three different resolutions are extracted and used as the input to the networks. To further reduce the false positives (FPs), we leverage the similarity between neighboring MRI slices, and adopt a weighted averaging strategy to aggregate the results obtained by the Siamese neural networks. The detection performance is evaluated on a set of 26 cases using a free-response receiver operating characteristic (FROC) analysis. The results show that the proposed approach correctly detects all the spinal metastatic lesions while producing only 0.40 FPs per case. At a true positive (TP) rate of 90%, the use of the aggregation reduces the FPs from 0.375 FPs per case to 0.207 FPs per case, a nearly 44.8% reduction. The results indicate that the proposed Siamese neural network method, combined with the aggregation strategy, provide a viable strategy for the automated detection of spinal metastasis in MRI images.

“…(55,56) Radiologists may overlook spine compression fractures if they do not routinely review sagittal midline images on body CT. (57) In response, a system was designed for the automated detection and localization of thoracic and lumbar vertebral body compression fractures on CT. (15,16,(59)(60)(61) In one such system, the sensitivity (and false-positive rate per patient) was 81% (2.1), 81% (1.3), and 76% (2.1) for sclerotic, lytic, and mixed lesions of the spine, respectively, using SVM classifiers. 5).…”

Section: Fracture Detectionmentioning

confidence: 99%

“…2 and 3). (15,16,(59)(60)(61) In one such system, the sensitivity (and false-positive rate per patient) was 81% (2.1), 81% (1.3), and 76% (2.1) for sclerotic, lytic, and mixed lesions of the spine, respectively, using SVM classifiers. (27) This system is a first step toward the quantitative analysis of metastatic spine disease for determination of tumor burden, assessment of lesion change over time, and inclusion of bone lesions into treatment response criteria such as RECIST.…”

Section: Bone Oncologymentioning

confidence: 99%

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

¹

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²

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³

2019

Journal of Bone and Mineral Research

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Artificial intelligence is upending many of our assumptions about the ability of computers to detect and diagnose diseases on medical images. Deep learning, a recent innovation in artificial intelligence, has shown the ability to interpret medical images with sensitivities and specificities at or near that of skilled clinicians for some applications. In this review, we summarize the history of artificial intelligence, present some recent research advances, and speculate about the potential revolutionary clinical impact of the latest computer techniques for bone and muscle imaging. © 2019 American Society for Bone and Mineral Research. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.

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