A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics

Shahedi, Maysam; Ma, Ling; Halicek, Martin; Guo, Rongrong; Zhang, Guoyi; Schuster, David M.; Nieh, Peter T.; Master, Viraj A.; Fei, Baowei

doi:10.1117/12.2293195

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…2 Several computerized algorithms have been developed recently to segment the prostate faster with higher repeatability compared to manual segmentation. [3][4][5][6][7][8][9][10][11][12][13][14][15] Some of these algorithms were learning-based segmentation techniques that used manual segmentation information of previously acquired CT images from the same patient for training. 9,10 This approach helps to have a more accurate segmentation.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based three-dimensional segmentation of the prostate on computed tomography images

Shahedi¹,

Halicek

Dormer³

et al. 2019

J. Med. Imag.

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Segmentation of the prostate in computed tomography (CT) is used for planning and guidance of prostate treatment procedures. However, due to the low soft-tissue contrast of the images, manual delineation of the prostate on CT is a time-consuming task with high interobserver variability. We developed an automatic, three-dimensional (3-D) prostate segmentation algorithm based on a customized U-Net architecture. Our dataset contained 92 3-D abdominal CT scans from 92 patients, of which 69 images were used for training and validation and the remaining for testing the convolutional neural network model. Compared to manual segmentation by an expert radiologist, our method achieved 83% AE 6% for Dice similarity coefficient (DSC), 2.3 AE 0.6 mm for mean absolute distance (MAD), and 1.9 AE 4.0 cm 3 for signed volume difference (ΔV). The average recorded interexpert difference measured on the same test dataset was 92% (DSC), 1.1 mm (MAD), and 2.1 cm 3 (ΔV). The proposed algorithm is fast, accurate, and robust for 3-D segmentation of the prostate on CT images.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based three-dimensional segmentation of the prostate on computed tomography images

Shahedi¹,

Halicek

Dormer³

et al. 2019

J. Med. Imag.

Self Cite

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Radiomics-guided radiation therapy: opportunities and challenges

Abdollahi

Chin²,

Clark

et al. 2022

Phys. Med. Biol.

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Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.

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Abdominal muscle segmentation from CT using a convolutional neural network

Edwards

Chhabra

Dormer

et al. 2020

Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging

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CT is widely used for diagnosis and treatment of a variety of diseases, including characterization of muscle loss. In many cases, changes in muscle mass, particularly abdominal muscle, indicate how well a patient is responding to treatment. Therefore, physicians use CT to monitor changes in muscle mass throughout the patient's course of treatment. In order to measure the muscle, radiologists must segment and review each CT slice manually, which is a time-consuming task. In this work, we present a fully convolutional neural network (CNN) for the segmentation of abdominal muscle on CT. We achieved a mean Dice similarity coefficient of 0.92, a mean precision of 0.93, and a mean recall of 0.91 in an independent test set. The CNN-based segmentation method can provide an automatic tool for the segmentation of abdominal muscle. As a result, the time required to obtain information about changes in abdominal muscle using the CNN takes a fraction of the time associated with manual segmentation methods and thus can provide a useful tool in the clinical application.

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A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics

Cited by 6 publications

References 19 publications

Deep learning-based three-dimensional segmentation of the prostate on computed tomography images

Deep learning-based three-dimensional segmentation of the prostate on computed tomography images

Radiomics-guided radiation therapy: opportunities and challenges

Abdominal muscle segmentation from CT using a convolutional neural network

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