Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks

Conze, Pierre-Henri; Kavur, Ali Emre; Gall, Émilie Cornec-Le; Gezer, Naciye Sinem; Meur, Yannick Le; Selver, M. Alper; Rousseau, François

doi:10.1016/j.artmed.2021.102109

Cited by 80 publications

(39 citation statements)

References 28 publications

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“…As established in many studies, deep learning excels at computer vision tasks (20)(21)(22), but deep learning studies often lack prospective validation and often fail to impact radiologic clinical routines. In contrast, our study includes external and postdeployment prospective validation and incorporates recommendations from the Checklist for AI in Medical Imaging (or, CLAIM) guidelines (23).…”

Section: Resultsmentioning

confidence: 99%

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Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI

Goel

Shih

Riyahi

et al. 2022

Radiology: Artificial Intelligence

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This study develops, validates, and deploys deep learning for automated total kidney volume (TKV) measurement (a marker of disease severity) on T2-weighted MRI studies of autosomal dominant polycystic kidney disease (ADPKD). The model was based on the U-Net architecture with an EfficientNet encoder, developed using 213 abdominal MRI studies in 129 patients with ADPKD. Patients were randomly divided into 70% training, 15% validation, and 15% test sets for model development. Model performance was assessed using Dice similarity coefficient (DSC) and Bland-Altman analysis. External validation in 20 patients from outside institutions demonstrated a DSC of 0.98 (IQR, 0.97-0.99) and a Bland-Altman difference of 2.6% (95% CI: 1.0%, 4.1%). Prospective validation in 53 patients demonstrated a DSC of 0.97 (IQR, 0.94-0.98) and a Bland-Altman difference of 3.6% (95% CI: 2.0%, 5.2%). Last, the efficiency of model-assisted annotation was evaluated on the first 50% of prospective cases (n = 28), with a 51% mean reduction in contouring time (P , .001), from 1724 seconds (95% CI: 1373, 2075) to 723 seconds (95% CI: 555, 892). In conclusion, our deployed artificial intelligence pipeline accurately performs automated segmentation for TKV estimation of polycystic kidneys and reduces expert contouring time.ClinicalTrials.gov identification no.: NCT00792155

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Section: Resultsmentioning

confidence: 99%

“…Conventionally, planimetry tracing (2) and stereologic methods (22) are used to measure TKV at MRI. These methods comprise a two-step process in which an initial organ contour is drawn, followed by tedious slice-by-slice correction of contour errors.…”

Section: Discussionmentioning

confidence: 99%

Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI

Goel

Shih

Riyahi

et al. 2022

Radiology: Artificial Intelligence

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“…cGAN (Mirza and Osindero, 2014 ) is an extension of GAN, which is used as a machine learning framework for training generative models. The proposed FSPET model adopts the framework in Conze et al ( 2021 ) based on cGAN, which consists of two neural networks: the generator G and the discriminator D.…”

Section: Methodsmentioning

confidence: 99%

Frontal Cortex Segmentation of Brain PET Imaging Using Deep Neural Networks

Zhan

Liu

et al. 2021

Front. Neurosci.

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18F-FDG positron emission tomography (PET) imaging of brain glucose use and amyloid accumulation is a research criteria for Alzheimer's disease (AD) diagnosis. Several PET studies have shown widespread metabolic deficits in the frontal cortex for AD patients. Therefore, studying frontal cortex changes is of great importance for AD research. This paper aims to segment frontal cortex from brain PET imaging using deep neural networks. The learning framework called Frontal cortex Segmentation model of brain PET imaging (FSPET) is proposed to tackle this problem. It combines the anatomical prior to frontal cortex into the segmentation model, which is based on conditional generative adversarial network and convolutional auto-encoder. The FSPET method is evaluated on a dataset of 30 brain PET imaging with ground truth annotated by a radiologist. Results that outperform other baselines demonstrate the effectiveness of the FSPET framework.

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“…The cGAN works fine when the number of training samples is limited. Conze et al [67] utilised cascaded pre-trained convolutional encoder-decoders as generators of cGAN for abdominal multi-organ segmentation, and considered the adversarial network as a discriminator to enforces the model to create realistic organ delineations.…”

Section: Figurementioning

confidence: 99%

Medical image segmentation using deep learning: A survey

Wang

Lei

Cui

et al. 2022

IET Image Processing

285

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Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation using deep learning techniques is presented. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi‐level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyse literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.

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Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks

Cited by 80 publications

References 28 publications

Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI

Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI

Frontal Cortex Segmentation of Brain PET Imaging Using Deep Neural Networks

Medical image segmentation using deep learning: A survey

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