Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 141)

This special bibliography lists 267 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1975

Texture analysis and Its applications in biomedical imaging: a survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021; date of current version January 24, 2022. This work was supported in part by the Portuguese Foundation for Science and Technology (FCT) under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

The importance of a protocol in the recovery and handling of burned human remains in a forensic context

Fire-related fatalities pose many investigative challenges, in part due to their fragility. This can be managed with the creation of protocols, specific to the environment in which they are implemented. Currently, no protocol for the recovery and handling of fire-related fatalities exists in Cape Town, South Africa. Additionally, the challenges, risk factors, and resources present at forensic scenes in the area have not been documented. From April to December of 2015, fire-related death scenes were attended with Salt River Forensic Pathology Laboratory, which serves the West Metropole of Cape Town. Details of the fatal fire scenes were noted, including the challenges faced, and the settings in which the fires occurred. Emphasis was placed on methodologies used to recover, handle, and transport remains, and the availability and utilisation of resources. The affect these methodologies had on the condition of the remains between scene and autopsy was assessed. In total 32 fire-related death scenes were attended, with 48 decedents recovered. Males predominated (64.6%), and the majority were young adults (75%). Accidental deaths were most prevalent (79.2%), however a fire-related suicide and homicides highlighted the importance of thorough investigation. Informal housing constituted 68.8% of the fatal fire scenes and presented unique scene constraints, including no direct road access at 50% of these scenes. Investigative limitations included: inadequate interagency communication, resulting in a lack of collateral information available at autopsy; deficient scene and contextual documentation; non-standardised recovery methodologies; insufficient availability and utilisation of resources (including safety equipment); and no specialised personnel (e.g. forensic pathologists/ anthropologists) conducting scene recovery. The majority of cases (60.4%) were further fragmented or fractured by time of autopsy, illustrating the necessity for improvement of current methodologies and the importance of the involvement of forensic anthropologists in recovery of fragmentary remains

Content aware multi-focus image fusion for high-magnification blood film microscopy

Automated digital high-magnification optical microscopy is key to accelerating biology research and improving pathology clinical pathways. High magnification objectives with large numerical apertures are usually preferred to resolve the fine structural details of biological samples, but they have a very limited depth-of-field. Depending on the thickness of the sample, analysis of specimens typically requires the acquisition of multiple images at different focal planes for each field-of-view, followed by the fusion of these planes into an extended depth-of-field image. This translates into low scanning speeds, increased storage space, and processing time not suitable for high-throughput clinical use. We introduce a novel content-aware multi-focus image fusion approach based on deep learning which extends the depth-of-field of high magnification objectives effectively. We demonstrate the method with three examples, showing that highly accurate, detailed, extended depth of field images can be obtained at a lower axial sampling rate, using 2-fold fewer focal planes than normally required

The Effect of Retrieval Practice on Vocabulary Learning for Children who are Deaf or Hard of Hearing

The goal of the current study was to determine if students who are deaf or hard of hearing (d/hh) would learn more new vocabulary words through the use of retrieval practice than repeated exposure (repeated study). No studies to date have used this cognitive strategy—retrieval practice—with children who are d/hh. Previous studies have shown that children with hearing loss struggle with learning vocabulary words. This deficit can negatively affect language development, reading outcomes, and overall academic success. Few studies have investigated specific interventions to address the poor vocabulary development for children with hearing loss. The current study investigated retrieval practice as a potentially effective strategy to increase word-learning for children who are d/hh and who use spoken language. It was found that children with hearing loss recalled a greater number of new vocabulary words when using retrieval practice than repeated exposure after a two day retention interval. This study also examined factors that influence whether a child remembers or forgets a word after a retention interval. Children who did not have an additional diagnosis recalled more words than children with an additional diagnosis. Children who were more efficient learners—took fewer trials to learn the word—recalled more words than children who were less efficient learners. High level of parent education and aided speech perception scores were not significant predictors of the children remembering the new words. In summary, this study was the first to show that retrieval practice caused students with hearing loss to learn more new vocabulary words than repeated exposure

The feasibility of using feature-flow and label transfer system to segment medical images with deformed anatomy in orthopedic surgery

In computer-aided surgical systems, to obtain high fidelity three-dimensional models, we require accurate segmentation of medical images. State-of-art medical image segmentation methods have been used successfully in particular applications, but they have not been demonstrated to work well over a wide range of deformities. For this purpose, I studied and evaluated medical image segmentation using the feature-flow based Label Transfer System described by Liu and colleagues. This system has produced promising results in parsing images of natural scenes. Its ability to deal with variations in shapes of objects is desirable. In this paper, we altered this system and assessed its feasibility of automatic segmentation. Experiments showed that this system achieved better recognition rates than those in natural-scene parsing applications, but the high recognition rates were not consistent across different images. Although this system is not considered clinically practical, we may improve it and incorporate it with other medical segmentation tools

S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction

The technology of hyperspectral imaging (HSI) records the visual information upon long-range-distributed spectral wavelengths. A representative hyperspectral image acquisition procedure conducts a 3D-to-2D encoding by the coded aperture snapshot spectral imager (CASSI) and requires a software decoder for the 3D signal reconstruction. By observing this physical encoding procedure, two major challenges stand in the way of a high-fidelity reconstruction. (i) To obtain 2D measurements, CASSI dislocates multiple channels by disperser-titling and squeezes them onto the same spatial region, yielding an entangled data loss. (ii) The physical coded aperture leads to a masked data loss by selectively blocking the pixel-wise light exposure. To tackle these challenges, we propose a spatial-spectral (S^2-) Transformer network with a mask-aware learning strategy. First, we simultaneously leverage spatial and spectral attention modeling to disentangle the blended information in the 2D measurement along both two dimensions. A series of Transformer structures are systematically designed to fully investigate the spatial and spectral informative properties of the hyperspectral data. Second, the masked pixels will induce higher prediction difficulty and should be treated differently from unmasked ones. Thereby, we adaptively prioritize the loss penalty attributing to the mask structure by inferring the pixel-wise reconstruction difficulty upon the mask-encoded prediction. We theoretically discusses the distinct convergence tendencies between masked/unmasked regions of the proposed learning strategy. Extensive experiments demonstrates that the proposed method achieves superior reconstruction performance. Additionally, we empirically elaborate the behaviour of spatial and spectral attentions under the proposed architecture, and comprehensively examine the impact of the mask-aware learning.Comment: 11 pages, 16 figures, 6 tables, Code: https://github.com/Jiamian-Wang/S2-transformer-HS

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 342)

This bibliography lists 208 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during October 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance