Segmentation of COVID-19 pneumonia lesions: A deep learning approach

An explainable and efficient deep learning framework for video anomaly detection

“…As discussed in Sect. 2 , PFPN provides instance segmentation and background segmentation and has proved to be a stable solution by being wildly used as a pre-trained model on many other fields [ 37 – 39 ]. We run this CNN-based model on the Detectron2 platform (The Facebook AI Research software system) [ 42 ].…”

“…For background segmentation, Kirilov et al propose Panoptic Feature Pyramid Networks (PFPN) [ 36 ] to solve the panoptic segmentation task (unifying instance segmentation and semantic segmentation). This model and its variant show great segmentation performance and have been used as the pre-trained models in tomography diagnose [ 37 ], real-time object detection [ 38 ], person detection [ 39 ], etc. Although ResNet and PFPN are popular when used as the pre-trained models, to our best knowledge, our work is the first video anomaly detection approach that uses them directly as pre-trained models without further fine-tune training process.…”

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

“…Many variations and small modifications of the original UNet architecture have been proposed [33,[152][153][154][155][156][157][158][159][160][161][162][163][164][165], including 3D variations coined VNet or 3D UNet [166][167][168][169][170][171][172][173][174][175][176][177][178][179] that are able to process cube patches. Some research fuses features or results from multiple views (2.5D) or multiple 2D and 3D networks attempting to capture information from different angles and dimensionalities [180][181][182][183][184][185][186][187][188][189][190]. Although UNet is prevalent in the literature, different architectures originated from the field of natural imaging segmentation, such as SegNet, DeepLab and Region CNNs, are also employed.…”

Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT

“…For example, segmentation of pneumonia infection regions in CT can be beneficial as a first step within detection and analysis methods based on convolutional neural networks (CNN) for coronavirus disease 2019 (COVID-19) ( Zhou et al, 2020 , Gao et al, 2020a , Amyar et al, 2020 , Harmon et al, 2020 , H.t. et al, 2020 , Paluru et al, 2021 , Tilborghs et al, 2020 , Ghomi et al, 2020 , Voulodimos et al, 2021a , Hasan et al, 2021 , Voulodimos et al, 2021b , Ranjbarzadeh et al, 2021 , Elharrouss et al, 2020 , Singh et al, 2021 , Yan et al, 2021 ). Health condition of many hospitalized COVID-19 patients often deteriorates and requires mechanical ventilation or high-flow oxygenation ( Lassau et al, 2021 ).…”