Non-rigid registration of a 3D ultrasound and a MR image data set of the female pelvic floor using a biomechanical model

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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Noninvasive patient tracker mask for spinal 3D navigation: does the required large-volume 3D scan involve a considerably increased radiation exposure?

Journal of Neurosurgery Spine ◽

10.3171/2020.5.spine20530 ◽

2020 ◽

Vol 33 (6) ◽

pp. 838-844

Author(s):

Jan-Helge Klingler ◽

Ulrich Hubbe ◽

Christoph Scholz ◽

Florian Volz ◽

Marc Hohenhaus ◽

...

Keyword(s):

Radiation Exposure ◽

Image Data ◽

3D Image ◽

Flat Panel ◽

3D Navigation ◽

Data Set ◽

Dose Area Product ◽

Flat Panel Detector ◽

3D Scan ◽

Area Product

OBJECTIVEIntraoperative 3D imaging and navigation is increasingly used for minimally invasive spine surgery. A novel, noninvasive patient tracker that is adhered as a mask on the skin for 3D navigation necessitates a larger intraoperative 3D image set for appropriate referencing. This enlarged 3D image data set can be acquired by a state-of-the-art 3D C-arm device that is equipped with a large flat-panel detector. However, the presumably associated higher radiation exposure to the patient has essentially not yet been investigated and is therefore the objective of this study.METHODSPatients were retrospectively included if a thoracolumbar 3D scan was performed intraoperatively between 2016 and 2019 using a 3D C-arm with a large 30 × 30–cm flat-panel detector (3D scan volume 4096 cm3) or a 3D C-arm with a smaller 20 × 20–cm flat-panel detector (3D scan volume 2097 cm3), and the dose area product was available for the 3D scan. Additionally, the fluoroscopy time and the number of fluoroscopic images per 3D scan, as well as the BMI of the patients, were recorded.RESULTSThe authors compared 62 intraoperative thoracolumbar 3D scans using the 3D C-arm with a large flat-panel detector and 12 3D scans using the 3D C-arm with a small flat-panel detector. Overall, the 3D C-arm with a large flat-panel detector required more fluoroscopic images per scan (mean 389.0 ± 8.4 vs 117.0 ± 4.6, p < 0.0001), leading to a significantly higher dose area product (mean 1028.6 ± 767.9 vs 457.1 ± 118.9 cGy × cm2, p = 0.0044).CONCLUSIONSThe novel, noninvasive patient tracker mask facilitates intraoperative 3D navigation while eliminating the need for an additional skin incision with detachment of the autochthonous muscles. However, the use of this patient tracker mask requires a larger intraoperative 3D image data set for accurate registration, resulting in a 2.25 times higher radiation exposure to the patient. The use of the patient tracker mask should thus be based on an individual decision, especially taking into considering the radiation exposure and extent of instrumentation.

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Faculty Opinions recommendation of An International Urogynecological Association (IUGA)/International Continence Society (ICS) joint report on the terminology for female pelvic floor dysfunction.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.5829956.5812054 ◽

2010 ◽

Author(s):

Hashim Hashim

Keyword(s):

Pelvic Floor ◽

Pelvic Floor Dysfunction ◽

International Continence Society ◽

Female Pelvic Floor ◽

International Urogynecological Association ◽

Female Pelvic Floor Dysfunction ◽

Joint Report

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Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

Remote Sensing ◽

10.3390/rs11101157 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1157 ◽

Cited By ~ 8

Author(s):

Jorge Fuentes-Pacheco ◽

Juan Torres-Olivares ◽

Edgar Roman-Rangel ◽

Salvador Cervantes ◽

Porfirio Juarez-Lopez ◽

...

Keyword(s):

Precision Agriculture ◽

Image Data ◽

Ground Truth ◽

Aerial Images ◽

Aerial Image ◽

Data Set ◽

Visual Appearance ◽

Aerial Robots ◽

Lighting Conditions ◽

Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

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ESR white paper: blockchain and medical imaging

Insights into Imaging ◽

10.1186/s13244-021-01029-y ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

◽

Elmar Kotter ◽

Luis Marti-Bonmati ◽

Adrian P. Brady ◽

Nandita M. Desouza

Keyword(s):

Medical Imaging ◽

Image Data ◽

Distributed Database ◽

Relevant Information ◽

White Paper ◽

Imaging Data ◽

Data Set ◽

Blockchain Technology ◽

Potential Applications ◽

Basic Technology

AbstractBlockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different “authors” including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.

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