Probe localization from ultrasound image sequences using deep learning for volume reconstruction

AbstractThe Institute of Virology at the Philipps-Universität Marburg is currently researching possible drugs to combat the Marburg virus. This involves classifying cell structures based on fluoroscopic microscopic image sequences. Conventionally, membranes of cells must be marked for better analysis, which is time consuming. In this work, an approach is presented to identify cell structures in images that are marked for subviral particles. It could be shown that there is a correlation between the distribution of subviral particles in an infected cell and the position of the cell’s structures. The segmentation is performed with a "Mask-R-CNN" algorithm, presented in this work. The model (a region-based convolutional neural network) is applied to enable a robust and fast recognition of cell structures. Furthermore, the network architecture is described. The proposed method is tested on data evaluated by experts. The results show a high potential and demonstrate that the method is suitable.

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A Deep Learning Approach for Prediction of IVF Implantation Outcome from Day 3 and Day 5 Time-Lapse Human Embryo Image Sequences

10.1109/icip42928.2021.9506097 ◽

2021 ◽

Author(s):

Mehryar Abbasi ◽

Parvaneh Saeedi ◽

Jason Au ◽

Jon Havelock

Keyword(s):

Deep Learning ◽

Human Embryo ◽

Time Lapse ◽

Image Sequences ◽

Learning Approach

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Reverse Scan Conversion and Efficient Deep Learning Network Architecture for Ultrasound Imaging on a Mobile Device

Sensors ◽

10.3390/s21082629 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2629

Author(s):

Kunkyu Lee ◽

Min Kim ◽

Changhyun Lim ◽

Tai-Kyong Song

Keyword(s):

Deep Learning ◽

Ultrasound Imaging ◽

Mobile Device ◽

Network Architecture ◽

Point Of Care ◽

Ultrasound Image ◽

Training Dataset ◽

Point Of Care Ultrasound ◽

Reverse Scan ◽

Scan Conversion

Point-of-care ultrasound (POCUS), realized by recent developments in portable ultrasound imaging systems for prompt diagnosis and treatment, has become a major tool in accidents or emergencies. Concomitantly, the number of untrained/unskilled staff not familiar with the operation of the ultrasound system for diagnosis is increasing. By providing an imaging guide to assist clinical decisions and support diagnosis, the risk brought by inexperienced users can be managed. Recently, deep learning has been employed to guide users in ultrasound scanning and diagnosis. However, in a cloud-based ultrasonic artificial intelligence system, the use of POCUS is limited due to information security, network integrity, and significant energy consumption. To address this, we propose (1) a structure that simultaneously provides ultrasound imaging and a mobile device-based ultrasound image guide using deep learning, and (2) a reverse scan conversion (RSC) method for building an ultrasound training dataset to increase the accuracy of the deep learning model. Experimental results show that the proposed structure can achieve ultrasound imaging and deep learning simultaneously at a maximum rate of 42.9 frames per second, and that the RSC method improves the image classification accuracy by more than 3%.

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