Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases

Belal, Sarah Lindgren; Sadik, May; Kaboteh, Reza; Enqvist, Olof; Ulén, Johannes; Poulsen, Mads Hvid; Simonsen, Jørn; Høilund‐Carlsen, Poul Flemming; Edenbrandt, Lars; Trägårdh, Elin

doi:10.1016/j.ejrad.2019.01.028

Cited by 105 publications

(67 citation statements)

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“…The RECOMIA platform and the deep learning-based tools for organ segmentations have already been used in several studies. Lindgren Belal et al [ 7 , 8 ] used bone segmentation for quantification of bone metastases PET/CT in patients with prostate cancer. The automatically measured tumour burden to bone was associated with overall survival.…”

Section: Discussionmentioning

confidence: 99%

“…The CNN-based organ segmentation in CT studies in RECOMIA has been used in multiple studies [ 7 – 12 ]. These studies were approved by the Regional Ethical Review Board (#295/08) and were performed following the Declaration of Helsinki.…”

Section: Methodsmentioning

confidence: 99%

“…These studies were approved by the Regional Ethical Review Board (#295/08) and were performed following the Declaration of Helsinki. Patients and image acquisition have been described previously [ 7 , 8 , 10 , 11 ].…”

Section: Methodsmentioning

confidence: 99%

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RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

et al. 2020

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Background: Artificial intelligence (AI) is about to transform medical imaging. The Research Consortium for Medical Image Analysis (RECOMIA), a not-for-profit organisation, has developed an online platform to facilitate collaboration between medical researchers and AI researchers. The aim is to minimise the time and effort researchers need to spend on technical aspects, such as transfer, display, and annotation of images, as well as legal aspects, such as de-identification. The purpose of this article is to present the RECOMIA platform and its AI-based tools for organ segmentation in computed tomography (CT), which can be used for extraction of standardised uptake values from the corresponding positron emission tomography (PET) image. Results: The RECOMIA platform includes modules for (1) local de-identification of medical images, (2) secure transfer of images to the cloud-based platform, (3) display functions available using a standard web browser, (4) tools for manual annotation of organs or pathology in the images, (5) deep learning-based tools for organ segmentation or other customised analyses, (6) tools for quantification of segmented volumes, and (7) an export function for the quantitative results. The AI-based tool for organ segmentation in CT currently handles 100 organs (77 bones and 23 soft tissue organs). The segmentation is based on two convolutional neural networks (CNNs): one network to handle organs with multiple similar instances, such as vertebrae and ribs, and one network for all other organs. The CNNs have been trained using CT studies from 339 patients. Experienced radiologists annotated organs in the CT studies. The performance of the segmentation tool, measured as mean Dice index on a manually annotated test set, with 10 representative organs, was 0.93 for all foreground voxels, and the mean Dice index over the organs were 0.86 (0.82 for the soft tissue organs and 0.90 for the bones). Conclusion: The paper presents a platform that provides deep learning-based tools that can perform basic organ segmentations in CT, which can then be used to automatically obtain the different measurement in the corresponding PET image. The RECOMIA platform is available on request at www.recomia.org for research purposes.

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confidence: 99%

Section: Methodsmentioning

confidence: 99%

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RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

et al. 2020

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“…Recently, convolutional neural networks (CNN) have been widely applied for image segmentation, classification, recognition, and image super-resolution. [ 9 – 14 ] Noise reduction techniques using CNN have also been proposed in the field of medical imaging. [ 15 ] Several studies have shown that deep learning-based super-resolution or denoise approaches were successfully applied to low-quality MR images to shorten imaging time.…”

Section: Introductionmentioning

confidence: 99%

Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network

et al. 2020

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We have developed a deep learning-based approach to improve image quality of single-shot turbo spin-echo (SSTSE) images of female pelvis. We aimed to compare the deep learning-based single-shot turbo spin-echo (DL-SSTSE) images of female pelvis with turbo spin-echo (TSE) and conventional SSTSE images in terms of image quality. One hundred five and 21 subjects were used as training and test sets, respectively. We performed 6-fold cross validation. In the training process, low-quality images were generated from TSE images as input. TSE images were used as ground truth images. In the test process, the trained convolutional neural network was applied to SSTSE images. The output images were denoted as DL-SSTSE images. Apart from DL-SSTSE images, classical filtering methods were adopted to SSTSE images. Generated images were denoted as F-SSTSE images. Contrast ratio (CR) of gluteal fat and myometrium and signal-to-noise ratio (SNR) of gluteal fat were measured for all images. Two radiologists graded these images using a 5-point scale and evaluated the image quality with regard to overall image quality, contrast, noise, motion artifact, boundary sharpness of layers in the uterus, and the conspicuity of the ovaries. CRs, SNRs, and image quality scores were compared using the Steel-Dwass multiple comparison tests. CRs and SNRs were significantly higher in DL-SSTSE, F-SSTSE, and TSE images than in SSTSE images. Scores with regard to overall image quality, contrast, noise, and boundary sharpness of layers in the uterus were significantly higher on DL-SSTSE and TSE images than on SSTSE images. There were no significant differences in the CRs, SNRs, and respective scores between DL-SSTSE and TSE images. The score with regard to motion artifacts was significantly higher on DL-SSTSE, F-SSTSE, and SSTSE images than on TSE images. The score with regard to the conspicuity of ovaries was significantly higher on DL-SSTSE images than on F-SSTSE, SSTSE, and TSE images (P < .001). DL-SSTSE images showed higher image quality as compared with SSTSE images. In comparison with conventional TSE images, DL-SSTSE images had acceptable image quality while keeping the advantage of the motion artifact-robustness and acquisition time efficiency in SSTSE imaging.

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“…In this sector, more than in any other medical imaging sector, the difficulty of reading images for clinicians is high, due to the limited spatial resolution and the low signal-to-noise ratio, making even more impacting the automation of image reading, analysis and interpretation when compared with other higher resolution, higher contrast imaging tools. State-of-the-art examples in a first category of ML algorithms, focused on the automation of specific radiology tasks, include the automatic detection of disease lesions [3][4][5][6] and segmentation or tissue differentiation [7][8][9][10][11]. A second category of ML algorithms finalized to the improvement of clinician performances, applied to nuclear medicine images, include automatic diagnosis and prognosis of specific diseases, and prediction of treatment response [12][13][14][15][16].…”

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The eye of nuclear medicine

Polidori

Salvatore

Castiglioni

et al. 2019

Clin Transl Imaging

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Over the last 50 years, nuclear medicine has undergone a profound evolution, marked in particular by the technological progress of scanning systems, equipped with increasingly performant scintillation detectors, transformed from planar to cylindrical geometries, from two-dimensional to three-dimensional configurations, coupled with ever more accurate reconstruction algorithms, with the aim of making the scanner's eye increasingly powerful in terms of resolution and sensitivity.Notwithstanding the inherent potential of PET, the most advanced among the nuclear medicine techniques for the quantification of physiological variables, such potential has never been fully exploited in clinical practice. In fact, quantification has not been deemed necessary by most nuclear medicine specialists who have relied mostly on their own experience-trained eyes, i.e., something hard to convert in validated and user friendly software for clinical practice. Moreover, there has been a lack of convincing evidence that quantitative PET measures of biochemical variables under pathological conditions were (1) possible and accurate and (2) more effective than "eye-based" analysis for patient management, thus the use of absolute quantification has been set aside and substituted by the use of semi-quantitative analyses such as the Standardized Uptake Value or the Metabolic Tumor Volume (or their derivatives).Nowadays, 40 years after the invention of PET, and more than 20 years of its clinical use, the time has come for a major leap forward based on a paradigm shift. Over the last 10 years, in view of the different prognosis and outcomes observed in patients with somewhat similar diagnosis, the potentials of artificial intelligence (AI) is being sought for an innovative and more practical, as well as effective, analysis of diagnostic imaging data. This revolutionary approach is based on the hypothesis that the analysis of the results of diagnostic investigations, including imaging data, when integrated with the information on treatment response and clinical endpoints obtained in large populations, could offer a totally new array of results and information for pursuing the goals of personalized precision medicine. This approach may completely revolutionize the way some primary objectives are pursued in the workup of individual patients including an early diagnosis and accurate staging, the definition of personalized treatment planning, the prognostic stratification, the prediction of the response to treatment, as well as the interim follow-up and restaging.The use technologies that allow to "read" medical images in a more objective and quantitative way, using AI and its subfield called machine learning (ML), paves the way for a major advancement in medicine. According to a series of papers published by the AI pioneer Arthur Samuel back in 1960 [1,2], ML gives computers the ability to learn (and to subsequently perform) a specific task without being explicitly programmed. The use of these intelligent systems makes it possible to completely reth...

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Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases

Cited by 105 publications

References 19 publications

RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network

The eye of nuclear medicine

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