A simple method for the automatic classification of body parts and detection of implanted metal using postmortem computed tomography scout view

Wada, Yuji; Morishita, Junji; Yoon, Yongsu; Okumura, Miki; Ikeda, Noriaki

doi:10.1007/s12194-020-00581-4

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Several algorithms have recently been proposed for the classification of anatomical regions in CT and MRI scans [11][12][13][14]. Among those, Ouyang et al [14] achieved the highest classification accuracy of 97.3% on their test dataset composed of 663 CT scans.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of a deep-learning based anatomical region labeling tool for Cone-Beam Computed Tomography scans in radiotherapy

Luximon

Neylon

Lamb

2023

Physics and Imaging in Radiation Oncology

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Section: Introductionmentioning

confidence: 99%

Feasibility of a deep-learning based anatomical region labeling tool for Cone-Beam Computed Tomography scans in radiotherapy

Luximon

Neylon

Lamb

2023

Physics and Imaging in Radiation Oncology

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“…In recent years, researchers have proposed several body part recognition methods, [18][19][20][21][22][23][24] which can be largely divided into three approaches. The first approach uses the prior knowledge of the gray value distribution in computed tomography (CT) images; 18 however, this approach is only suitable for CT images.…”

Section: Introductionmentioning

confidence: 99%

“…These methods often extract hand‐crafted features, followed by the use of a classifier, such as AdaBoost 19,20 or random forest 21 . The last approach is the traditional machine learning method to automatically recognize body parts using specially designed features and algorithms 22–24 . However, owing to the large variation in the body parts of different individuals, it is difficult to specifically design common features to achieve a robust recognition performance.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based body part recognition algorithm for three‐dimensional medical images

et al. 2022

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Background The automatic recognition of human body parts in three‐dimensional medical images is important in many clinical applications. However, methods presented in prior studies have mainly classified each two‐dimensional (2D) slice independently rather than recognizing a batch of consecutive slices as a specific body part. Purpose In this study, we aim to develop a deep learning‐based method designed to automatically divide computed tomography (CT) and magnetic resonance imaging (MRI) scans into five consecutive body parts: head, neck, chest, abdomen, and pelvis. Methods A deep learning framework was developed to recognize body parts in two stages. In the first preclassification stage, a convolutional neural network (CNN) using the GoogLeNet Inception v3 architecture and a long short‐term memory (LSTM) network were combined to classify each 2D slice; the CNN extracted information from a single slice, whereas the LSTM employed rich contextual information among consecutive slices. In the second postprocessing stage, the input scan was further partitioned into consecutive body parts by identifying the optimal boundaries between them based on the slice classification results of the first stage. To evaluate the performance of the proposed method, 662 CT and 1434 MRI scans were used. Results Our method achieved a very good performance in 2D slice classification compared with state‐of‐the‐art methods, with overall classification accuracies of 97.3% and 98.2% for CT and MRI scans, respectively. Moreover, our method further divided whole scans into consecutive body parts with mean boundary errors of 8.9 and 3.5 mm for CT and MRI data, respectively. Conclusions The proposed method significantly improved the slice classification accuracy compared with state‐of‐the‐art methods, and further accurately divided CT and MRI scans into consecutive body parts based on the results of slice classification. The developed method can be employed as an important step in various computer‐aided diagnosis and medical image analysis schemes.

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“…Therefore, biometrics for healthcare, a simple task for various patients, is preferred [4][5][6][7]. Several studies have reported biometric applications in radiologic technology [1][2][3][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] and forensic pathology [28][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Biological fingerprint for patient verification using trunk scout views at various scan ranges in computed tomography

Ueda

Morishita

Kudomi

2022

Radiol Phys Technol

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Immediate verification of whether a patient being examined is correct is desirable, even if the scan ranges change during different examinations for the same patient. This study proposes an advanced biological fingerprint technique for the rapid and reliable verification of various scan ranges in computed tomography (CT) scans of the torso of the same patient. The method comprises the following steps: geometric correction of different scans, local feature extraction, mismatch elimination, and similarity evaluation. The geometric magnification correction was aligned at the scanner table height in the first two steps, and the local maxima were calculated as the local features. In the third step, local features from the follow-up scout image are matched to those in the corresponding baseline scout image via template matching and outlier elimination via a robust estimator. We evaluated the correspondence rate based on the inlier ratio between corresponding scout images. The ratio of inliers between the baseline and follow-up scout images was assessed as the similarity score. The clinical dataset, including chest, abdomen-pelvis, and chest-abdomenpelvis scans, included 600 patients (372 men, 68 ± 12 years) who underwent two routine torso CT examinations. The highest area under the receiver operating characteristic curve (AUC) was 0.996, which was sufficient for patient verification. Moreover, the verification results were comparable to the conventional method, which uses scout images in the same scan range. Patient identity verification was achieved before the main scan, even in follow-up torso CT, under different scan ranges.

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A simple method for the automatic classification of body parts and detection of implanted metal using postmortem computed tomography scout view

Cited by 6 publications

References 9 publications

Feasibility of a deep-learning based anatomical region labeling tool for Cone-Beam Computed Tomography scans in radiotherapy

Feasibility of a deep-learning based anatomical region labeling tool for Cone-Beam Computed Tomography scans in radiotherapy

Deep learning‐based body part recognition algorithm for three‐dimensional medical images

Biological fingerprint for patient verification using trunk scout views at various scan ranges in computed tomography

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