Search

Deep learning, a new branch of machine learning algorithm, has emerged as a fast growing trend in medical imaging and become the state-of-the-art method in various clinical applications such as Radiology, Histo-pathology and Radiation Oncology. Specifically in radiation oncology, deep learning has shown its power in performing automatic segmentation tasks in radiation therapy for Organs-At-Risks (OAR), given its potential in improving the efficiency of OAR contouring and reducing the inter- and intra-observer variabilities. The similar interests were shared for target volume segmentation, an essential step of radiation therapy treatment planning, where the gross tumor volume is defined and microscopic spread is encompassed. The deep learning-based automatic segmentation method has recently been expanded into target volume automatic segmentation. In this paper, the authors summarized the major deep learning architectures of supervised learning fashion related to target volume segmentation, reviewed the mechanism of each infrastructure, surveyed the use of these models in various imaging domains (including Computational Tomography with and without contrast, Magnetic Resonant Imaging and Positron Emission Tomography) and multiple clinical sites, and compared the performance of different models using standard geometric evaluation metrics. The paper concluded with a discussion of open challenges and potential paths of future research in target volume automatic segmentation and how it may benefit the clinical practice., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/qims-21-168). The special issue “Artificial Intelligence for Image-guided Radiation Therapy” was commissioned by the editorial office without any funding or sponsorship. LD reports NIH grants for research in proton therapy and outcome studies, unrelated to this work. Sponsored research and honoraria from Varian Medical System. TL reports consulting fees, honoraria, and travel expenses from Varian Medical Systems unrelated to this work. Patent titled “Systems and methods for automatic, customized radiation treatment plan generation for cancer” was filed. The authors have no other conflicts of interest to declare., (2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

The ability to accurately design carbon nanofibre (CN) field emitters with predictable electron emission characteristics will enable their use as electron sources in various applications such as microwave amplifiers, electron microscopy, parallel beam electron lithography and advanced Xray sources. Here, highly uniform CN arrays of controlled diameter, pitch and length were fabricated using plasma enhanced chemical vapour deposition and their individual emission characteristics and field enhancement factors were probed using scanning anode field emission mapping. For a pitch of 10 µm and a CN length of 5 µm, the directly measured enhancement factors of individual CNs was 242, which was in excellent agreement with conventional geometry estimates (240). We show here direct empirical evidence that in regular arrays of vertically aligned CNs the overall enhancement factor is reduced when the pitch between emitters is less than half the emitter height, in accordance to our electrostatic simulations. Individual emitters showed narrow Gaussian-like field enhancement distributions, in excellent agreement with electric field simulations.

Reconfigurable liquid crystal microlenses employing arrays of multiwalled carbon nanotubes (MWNTs) have been designed and fabricated. The cells consist of arrays of 2 microm high MWNTs grown by plasma-enhanced chemical vapor deposition on silicon with a top electrode of indium tin oxide coated glass positioned 20 microm above the silicon and the gap filled with the nematic liquid crystal BLO48. Simulations have found that, while its nematic liquid crystal aligns with MWNTs within a distance of 10nm, this distance is greatly enhanced by the application of an external electric field. Polarized light experiments show that light is focused with focal lengths ranging from approximately 7 microm to 12 microm.

To communicate, spacecraft and satellites rely on microwave devices, which at present are based on relatively inefficient thermionic electron sources that require heating and cannot be switched on instantaneously. Here we describe a microwave diode that uses a cold-cathode electron source consisting of carbon nanotubes and that operates at high frequency and at high current densities. Because it weighs little, responds instantaneously and has no need of heating, this miniaturized electron source should prove valuable for microwave devices used in telecommunications.