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1. A TBP DICOM format for total-body scanner-independent lesion evolution detection

   Xue, M; Huang, W; Tashayyod, D; Kang, J; Gandjbakhche, A; Armand, M (March 2025, PROCEEDINGS VOLUME 13313 BIOS | 25-31 JANUARY 2025 Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables VI)

   Total-body photography (TBP) has the potential to revolutionize early detection of skin cancers by monitoring minute changes in lesions over time. However, there is no standardized Digital Imaging and Communications in Medicine (DICOM) format for TBP. In order to accommodate various TBP data types and sophisticated data preprocessing pipelines, we propose three TBP Extended Information Object Definitions (IODs) for 2D regional images, dermoscopy images, and 3D surface meshes. We introduce a comprehensive pipeline integrating advanced image processing techniques, including 3D DICOM representation, super-resolution enhancement, and style transfer for dermoscopic-like visualization. Our framework tracks individual lesions across multiple TBP scans from different imaging systems and provides cloud-based storage with a customized DICOM viewer. To demonstrate the effectiveness of our approach, we validate our framework using TBP datasets from multiple imaging systems. Our framework and proposed IODs enhance TBP interoperability and clinical utility in dermatological practice, potentially improving early skin cancer detection. 

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2. Computational metadata generation methods for biological specimen image collections

   https://doi.org/10.1007/s00799-022-00342-1  

   Karnani, Kevin; Pepper, Joel; Bakiş, Yasin; Wang, Xiaojun; Bart, Henry; Breen, David E.; Greenberg, Jane (November 2022, International Journal on Digital Libraries)

   Adam, N.; Neuhold, E.; Furuta, R. (Ed.)

   Metadata is a key data source for researchers seeking to apply machine learning (ML) to the vast collections of digitized biological specimens that can be found online. Unfortunately, the associated metadata is often sparse and, at times, erroneous. This paper extends previous research conducted with the Illinois Natural History Survey (INHS) collection (7244 specimen images) that uses computational approaches to analyze image quality, and then automatically generates 22 metadata properties representing the image quality and morphological features of the specimens. In the research reported here, we demonstrate the extension of our initial work to University of the Wisconsin Zoological Museum (UWZM) collection (4155 specimen images). Further, we enhance our computational methods in four ways: (1) augmenting the training set, (2) applying contrast enhancement, (3) upscaling small objects, and (4) refining our processing logic. Together these new methods improved our overall error rates from 4.6 to 1.1%. These enhancements also allowed us to compute an additional set of 17 image-based metadata properties. The new metadata properties provide supplemental features and information that may also be used to analyze and classify the fish specimens. Examples of these new features include convex area, eccentricity, perimeter, skew, etc. The newly refined process further outperforms humans in terms of time and labor cost, as well as accuracy, providing a novel solution for leveraging digitized specimens with ML. This research demonstrates the ability of computational methods to enhance the digital library services associated with the tens of thousands of digitized specimens stored in open-access repositories world-wide by generating accurate and valuable metadata for those repositories. 

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3. A comprehensive review of computational diagnostic techniques for lymphedema

   https://doi.org/10.1088/2516-1091/ada85a  

   K_R, Jayasree; Vijayakumar, D K; Sugumaran, Vijayan; Pathinarupothi, Rahul Krishnan (February 2025, Progress in Biomedical Engineering)

   Abstract Lymphedema is localized swelling due to lymphatic system dysfunction, often affecting arms and legs due to fluid accumulation. It occurs in 20% to 94% of patients within 2–5 years after breast cancer treatment, with around 20% of women developing breast cancer-related lymphedema. This condition involves the accumulation of protein-rich fluid in interstitial spaces, leading to symptoms like swelling, pain, and reduced mobility that significantly impact quality of life. The early diagnosis of lymphedema helps mitigate the risk of deterioration and prevent its progression to more severe stages. Healthcare providers can reduce risks through exercise prescriptions and self-manual lymphatic drainage techniques. Lymphedema diagnosis currently relies on physical examinations and limb volume measurements, but challenges arise from a lack of standardized criteria and difficulties in detecting early stages. Recent advancements in computational imaging and decision support systems have improved diagnostic accuracy through enhanced image reconstruction and real-time data analysis. The aim of this comprehensive review is to provide an in-depth overview of the research landscape in computational diagnostic techniques for lymphedema. The computational techniques primarily include imaging-based, electrical, and machine learning (ML) approaches, which utilize advanced algorithms and data analysis. These modalities were compared based on various parameters to choose the most suitable techniques for their applications. Lymphedema detection faces challenges like subtle symptoms and inconsistent diagnostics. The research identifies bioimpedance spectroscopy (BIS), Kinect sensor and ML integration as the promising modalities for early lymphedema detection. BIS can effectively identify lymphedema as early as four months post-surgery with sensitivity of 44.1% and specificity of 95.4% in diagnosing lymphedema whereas ML and artificial neural network achieved an impressive average cross-validation accuracy of 93.75%, with sensitivity at 95.65% and specificity at 91.03%. ML and imaging can be integrated into clinical practice to enhance diagnostic accuracy and accessibility. 

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4. Diffusion probabilistic generative models for accelerated, in‐NICU permanent magnet neonatal MRI

   https://doi.org/10.1002/mrm.30585  

   Arefeen, Yamin; Levac, Brett; Patel, Bhairav; Ho, Chang; Tamir, Jonathan I (June 2025, Magnetic Resonance in Medicine)

   Purpose: Magnetic Resonance Imaging (MRI) enables non‐invasive assessment of brain abnormalities during early life development. Permanent magnet scanners operating in the neonatal intensive care unit (NICU) facilitate MRI of sick infants, but have long scan times due to lower signal‐to‐noise ratios (SNR) and limited receive coils. This work accelerates in‐NICU MRI with diffusion probabilistic generative models by developing a training pipeline accounting for these challenges. Methods: We establish a novel training dataset of clinical, 1 Tesla neonatal MR images in collaboration with Aspect Imaging and Sha'are Zedek Medical Center. We propose a pipeline to handle the low quantity and SNR of our real‐world dataset (1) modifying existing network architectures to support varying resolutions; (2) training a single model on all data with learned class embedding vectors; (3) applying self‐supervised denoising before training; and (4) reconstructing by averaging posterior samples. Retrospective under‐sampling experiments, accounting for signal decay, evaluated each item of our proposed methodology. A clinical reader study with practicing pediatric neuroradiologists evaluated our proposed images reconstructed from under‐sampled data. Results: Combining all data, denoising pre‐training, and averaging posterior samples yields quantitative improvements in reconstruction. The generative model decouples the learned prior from the measurement model and functions at two acceleration rates without re‐training. The reader study suggests that proposed images reconstructed from under‐sampled data are adequate for clinical use. Conclusion: Diffusion probabilistic generative models applied with the proposed pipeline to handle challenging real‐world datasets could reduce the scan time of in‐NICU neonatal MRI. 

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5. MRI Image-Based Mapping of Human Head Motion and Brain Ventricular Cerebrospinal Fluid Flows Using Computer Vision

   https://doi.org/10.1007/978-3-031-94962-3_19  

   Xu, Jin; Liou, William W; Yamada, Shinya; Nakajima, Madoka; Miyajima, Masakazu; Horikoshi, Ko (January 2025, Springer Nature Switzerland)

   Cerebrospinal fluid (CSF) plays a critical role in brain metabolism and protection from external forces. Traditional MRI can provide some insights into CSF dynamics; however, more advanced and cost-effective methods are needed for precise and comprehensive visualization of flow patterns, velocities, and directions in clinical settings. In this paper, we demonstrate a new application of a few open-source computer vision software packages to capture CSF motion from time spatial inversion pulse (Time-SLIP) MRI clinical images (in DICOM format). To test the hypothesis that the CSF flow depends on head motions, a reliable and robust pipeline of processing Time-SLIP MRI images is developed to extract both anatomy and CSF motion dynamics. The paper presents a methodology for extracting unsteady flow information from Time-SLIP MRI images and the results of its application. The results show that the computer vision method can be applied to extract unsteady CSF flow information. We also discuss observations and identify future areas for improvement by integrating CFD simulations for validation as a vital component for studying CSF dynamics. 

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