Context: Whole slide imaging (WSI) for digital pathology involves the rapid automated acquisition of multiple high-power fields from a microscope slide containing a tissue specimen. Capturing each field in the correct focal plane is essential to create high-quality digital images. Others have described a novel focusing method which reduces the number of focal planes required to generate accurate focus. However, this method was not applied dynamically in an automated WSI system under continuous motion.
Aims: This report measures the accuracy of this method when applied in a rapid continuous scan mode using a dual sensor WSI system with interleaved acquisition of images.
Methods: We acquired over 400 tiles in a "stop and go" scan mode, surveying the entire z depth in each tile and used this as ground truth. We compared this ground truth focal height to the focal height determined using a rapid 3-point focus algorithm applied dynamically in a continuous scanning mode.
Results: Our data showed the average focal height error of 0.30 (±0.27) μm compared to ground truth, which is well within the system's depth of field. On a tile by tile assessment, approximately 95% of the tiles were within the system's depth of field. Further, this method was six times faster than acquiring tiles compared to the same method in a non-continuous scan mode.
Conclusions: The data indicates that the method employed can yield a significant improvement in scan speed while maintaining highly accurate autofocusing.
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http://dx.doi.org/10.4103/2153-3539.84231 | DOI Listing |
J Clin Ultrasound
January 2025
JD Hamilton Consulting, Brighton, Michigan, USA.
Background: Ultrasound lung surface motion measurement is valuable for the evaluation of a variety of diseases. Speckle tracking or Doppler-based techniques are limited by the loss of visualization as a tracked point moves under ribs or is dependent.
Methods: We developed a synthetic lateral phase-based algorithm for tracking lung motion to overcome these limitations.
BMC Oral Health
January 2025
Center for Plastic & Reconstructive Surgery, Department of Stomatology, Affiliated People's Hospital, Zhejiang Provincial People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
Background: The purpose of this study was to evaluate the validity of near-infrared light reflection for detecting different depths of proximal caries in posterior teeth and to compare it with commonly used clinical oral examinations and bitewing radiography images.
Methods: Twenty-six patients with a total of 516 proximal surfaces were included in this study. The ground truth of the proximal caries was determined through a consensus reached by two experienced dentists after an intraoral examination assisted by bitewing radiographs.
Am J Orthod Dentofacial Orthop
February 2025
Department of Orthodontics, Faculty of Dentistry, Çanakkale Onsekiz Mart University, Çanakkale, Turkey.
Introduction: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible.
Methods: A total of 55 cone-beam computed tomography scans that met the inclusion criteria were collected and divided into test and training groups. The MONAI (Medical Open Network for Artificial Intelligence) Label active learning tool extension was used to train the automatic model.
Magn Reson Imaging
January 2025
Institute of Fluid Mechanics, University of Rostock, Rostock, Germany.
Purpose: To improve the current method for MRI turbulence quantification which is the intravoxel phase dispersion (IVPD) method. Turbulence is commonly characterized by the Reynolds stress tensor (RST) which describes the velocity covariance matrix. A major source for systematic errors in MRI is the sequence's sensitivity to the variance of the derivatives of velocity, such as the acceleration variance, which can lead to a substantial measurement bias.
View Article and Find Full Text PDFNeural Netw
January 2025
Luca Healthcare R&D, Shanghai, 200000, China. Electronic address:
Due to data privacy and storage concerns, Source-Free Unsupervised Domain Adaptation (SFUDA) focuses on improving an unlabelled target domain by leveraging a pre-trained source model without access to source data. While existing studies attempt to train target models by mitigating biases induced by noisy pseudo labels, they often lack theoretical guarantees for fully reducing biases and have predominantly addressed classification tasks rather than regression ones. To address these gaps, our analysis delves into the generalisation error bound of the target model, aiming to understand the intrinsic limitations of pseudo-label-based SFUDA methods.
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