Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps

Togo, Ren; Hirata, Kenji; Manabe, Osamu; Ohira, Hiroshi; Tsujino, Ichizo; Magota, Keiichi; Ogawa, Takahiro; Haseyama, Miki; Shiga, Tohru

doi:10.1016/j.compbiomed.2018.11.008

Cited by 38 publications

(10 citation statements)

References 33 publications

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“…Togo et al did a similar analysis using PET/CT images, also been successful with the use of polar maps. The idea was to assess the ML model (in case, deep learning) to distinguish between two different outcomes: cardiac sarcoidosis and non-cardiac sarcoidosis ( 50 ).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps

Filho

Fernandes

Wiefels

et al. 2021

Front. Cardiovasc. Med.

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Myocardial perfusion imaging (MPI) plays an important role in patients with suspected and documented coronary artery disease (CAD). Machine Learning (ML) algorithms have been developed for many medical applications with excellent performance. This study used ML algorithms to discern normal and abnormal gated Single Photon Emission Computed Tomography (SPECT) images. We analyzed one thousand and seven polar maps from a database of patients referred to a university hospital for clinically indicated MPI between January 2016 and December 2018. These studies were reported and evaluated by two different expert readers. The image features were extracted from a specific type of polar map segmentation based on horizontal and vertical slices. A senior expert reading was the comparator (gold standard). We used cross-validation to divide the dataset into training and testing subsets, using data augmentation in the training set, and evaluated 04 ML models. All models had accuracy >90% and area under the receiver operating characteristics curve (AUC) >0.80 except for Adaptive Boosting (AUC = 0.77), while all precision and sensitivity obtained were >96 and 92%, respectively. Random Forest had the best performance (AUC: 0.853; accuracy: 0,938; precision: 0.968; sensitivity: 0.963). ML algorithms performed very well in image classification. These models were capable of distinguishing polar maps remarkably into normal and abnormal.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps

Filho

Fernandes

Wiefels

et al. 2021

Front. Cardiovasc. Med.

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“…Following general images, medical images are the next area where the technology is expected to be applied to society [ 42 , 43 , 44 ]. Medical images are highly specialized due to the clarification of imaging standards, but the quality of the captured images is high.…”

Section: Related Workmentioning

confidence: 99%

Defect Detection of Subway Tunnels Using Advanced U-Net Network

Wang

Togo

Ogawa

et al. 2022

Sensors

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In this paper, we present a novel defect detection model based on an improved U-Net architecture. As a semantic segmentation task, the defect detection task has the problems of background–foreground imbalance, multi-scale targets, and feature similarity between the background and defects in the real-world data. Conventionally, general convolutional neural network (CNN)-based networks mainly focus on natural image tasks, which are insensitive to the problems in our task. The proposed method has a network design for multi-scale segmentation based on the U-Net architecture including an atrous spatial pyramid pooling (ASPP) module and an inception module, and can detect various types of defects compared to conventional simple CNN-based methods. Through the experiments using a real-world subway tunnel image dataset, the proposed method showed a better performance than that of general semantic segmentation including state-of-the-art methods. Additionally, we showed that our method can achieve excellent detection balance among multi-scale defects.

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“…Within the cardiac imaging field, a DL model (composed of convolutional and fully connected layers) and SPECT myocardial perfusion imaging were used to predict the presence of obstructive coronary artery disease [ 68 ]. Additionally, a pretrained Inception-v3 network [ 69 ] was utilized to extract high-level features from polar maps that following feature selection was then inputted to an SVM model to classify between cardiac sarcoidosis (CS) and non-CS [ 70 ]. Meanwhile, in the oncology field, PET data and CNN networks were used to predict treatment response in esophageal cancer patients after neoadjuvant chemotherapy [ 71 ] and radio-chemotherapy [ 72 ] and in chemoradiotherapy treated cervical cancer patients [ 73 ].…”

Section: Overview Of Deep Learning Applications In Medical Imagingmentioning

confidence: 99%

Application and Construction of Deep Learning Networks in Medical Imaging

Torres-Velázquez

Chen

et al. 2021

IEEE Trans. Radiat. Plasma Med. Sci.

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Deep learning (DL) approaches are part of the machine learning (ML) subfield concerned with the development of computational models to train artificial intelligence systems. DL models are characterized by automatically extracting high-level features from the input data to learn the relationship between matching datasets. Thus, its implementation offers an advantage over common ML methods that often require the practitioner to have some domain knowledge of the input data to select the best latent representation. As a result of this advantage, DL has been successfully applied within the medical imaging field to address problems such as disease classification and tumor segmentation for which it is difficult or impossible to determine which image features are relevant. Therefore, taking into consideration the positive impact of DL on the medical imaging field, this paper reviews the key concepts associated with its evolution and implementation. The sections of this review summarize the milestones related to the development of the DL field, followed by a description of the elements of deep neural network and an overview of its application within the medical imaging field. Subsequently, the key steps necessary to implement a supervised DL application are defined, and associated limitations are discussed.

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Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps

Cited by 38 publications

References 33 publications

Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps

Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps

Defect Detection of Subway Tunnels Using Advanced U-Net Network

Application and Construction of Deep Learning Networks in Medical Imaging

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