Anomaly detection in chest 18F-FDG PET/CT by Bayesian deep learning

Nakao, Takahiro; Hanaoka, Shouhei; Nomura, Yukihiro; Hayashi, Naoto; Abe, Osamu

doi:10.1007/s11604-022-01249-2

Cited by 19 publications

(12 citation statements)

References 23 publications

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“…Proposed method successfully realized 4.63 dB of lesion contrast enhancement, better lesion detection performance than direct analysis of original PET image, though the unsupervised learning-based method. The training dataset size was about 5% of that used in the related study with Bayesian 2D U-Net by Nakao et al [4]. Furthermore, the 2.5D lesion enhancement processing is a method that could consider 3D anatomical structures with smaller computational cost and GPU memory usage than direct 3D image analysis.…”

Section: Discussionmentioning

confidence: 99%

“…AnoGAN proposed by Schlegl et al is a generative adversarial network (GAN) based anomaly detection methods and has shown promising results for optical coherence tomography images processing [3]. Nakao et al proposed a lung lesion detection method using 2D Bayesian U-Net and anomaly detection in FDG-PET/CT images [4]. However, this method requires more than 1,800 normal cases for training.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised lung lesion detection on FDG-PET/CT images by deep image transformation-based 2.5-dimensional local anomaly detection

Segawa,

Nemoto,

Kaida

et al. 2024

Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications

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We propose an unsupervised method to detect lung lesions on FDG-PET/CT images based on deep image anomaly detection using 2.5-dimensional (2.5D) image processing. This 2.5D processing is applied to preprocessed FDG-PET/CT images without image patterns other than lung fields. It enhances lung lesions by parallel analysis of axial, coronal, and sagittal FDG-PET/CT slice images using multiple 2D U-Net. All the U-Nets are pretrained by 95 cases of normal FDG-PET/CT images having no lung lesions and used to transform CT slice images to normal FDG-PET slice images without any lesion-like SUV patterns. A lesion-enhanced image is obtained by merging subtractions of the transformed three normal FDG-PET images from the input FDG-PET image. Lesion detection is performed by simple binarization of the lesion-enhanced images. The threshold value varies from the case and is the 30-percentile voxel value of the target lesion-enhanced image. In each extracted region, the average of the intra-regional voxel values of the enhanced image is computed and assigned as a lesion-like score. We evaluated the proposed method by 27 patients FDG-PET/CT images with 41 lung lesions. The proposed method achieved 82.9 % of lesion detection sensitivity with five false positives per case. The result was significantly superior to the detection performance of FDG-PET image thresholding and indicates that the proposed method may be helpful for effective lung lesion detection. Future works include expanding the detectable range of lesions to outside lungs, such as mediastinum and axillae.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unsupervised lung lesion detection on FDG-PET/CT images by deep image transformation-based 2.5-dimensional local anomaly detection

Segawa,

Nemoto,

Kaida

et al. 2024

Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications

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“…However, they are known from probabilistic segmentation approaches. For example the Probabilistic Hierarchical Segmentation (PHISeg) combines a conditional variational autoencoder (cVAE) with a U-NET setup proposed by [4], Bayesian U-Nets [23] can model epistemic uncertainty with weak labels and Monte Carlo estimates [20,5,19].…”

Section: Related Workmentioning

confidence: 99%

Confidence-Aware and Self-Supervised Image Anomaly Localisation

Müller¹,

Baugh²,

Tan³

et al. 2023

Preprint

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Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis. It is possible to learn an expected distribution from a single class of normative samples, e.g., through epistemic uncertainty estimates, auto-encoding models, or from synthetic anomalies in a self-supervised way. The performance of self-supervised anomaly detection approaches is still inferior compared to methods that use examples from known unknown classes to shape the decision boundary. However, outlier exposure methods often do not identify unknown unknowns. Here we discuss an improved self-supervised single-class training strategy that supports the approximation of probabilistic inference with loosen feature locality constraints. We show that up-scaling of gradients with histogram-equalised images is beneficial for recently proposed self-supervision tasks. Our method is integrated into several out-of-distribution (OOD) detection models and we show evidence that our method outperforms the state-of-the-art on various benchmark datasets. Source code will be publicly available by the time of the conference.

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“…The models' performance for classification and segmentation was not compared against other state-ofthe-art methods, although multiple different hyperparameters were tested. Nakao et al [86] employed BDL for anomaly detection in chest PET/CT scans using F-fluorodeoxyglucose tracers. The proposed model used MC-Dropout as a Bayesian approximation method to sample from the posterior distribution.…”

Section: ) Other Tasks In Medical Imagingmentioning

confidence: 99%

A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges

2022

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In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence, and it has been deployed in different fields of healthcare applications such as image processing, natural language processing, and signal processing. DL models have also been intensely used in different tasks of healthcare such as disease diagnostics and treatments. Deep learning techniques have surpassed other machine learning algorithms and proved to be the ultimate tools for many state-of-the-art applications. Despite all that success, classical deep learning has limitations and their models tend to be very confident about their predicted decisions because it does not know when it makes mistake. For the healthcare field, this limitation can have a negative impact on models predictions since almost all decisions regarding patients and diseases are sensitive. Therefore, Bayesian deep learning (BDL) has been developed to overcome these limitations. Unlike classical DL, BDL uses probability distributions for the model parameters, which makes it possible to estimate the whole uncertainties associated with the predicted outputs. In this regard, BDL offers a rigorous framework to quantify all sources of uncertainties in the model. This study reviews popular techniques of using Bayesian deep learning with their benefits and limitations. It also reviewed recent deep learning architecture such as Convolutional Neural Networks and Recurrent Neural Networks. In particular, the applications of Bayesian deep learning in healthcare have been discussed such as its use in medical imaging tasks, clinical signal processing, medical natural language processing, and electronic health records. Furthermore, this paper has covered the deployment of Bayesian deep learning for some of the widespread diseases. This paper has also discussed the fundamental research challenges and highlighted some research gaps in both the Bayesian deep learning and healthcare perspective.

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Anomaly detection in chest 18F-FDG PET/CT by Bayesian deep learning

Cited by 19 publications

References 23 publications

Unsupervised lung lesion detection on FDG-PET/CT images by deep image transformation-based 2.5-dimensional local anomaly detection

Unsupervised lung lesion detection on FDG-PET/CT images by deep image transformation-based 2.5-dimensional local anomaly detection

Confidence-Aware and Self-Supervised Image Anomaly Localisation

A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges

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