Dataset Information

PoreScript: Semi-automated pore size algorithm for scaffold characterization.

ABSTRACT: The scaffold pore size influences many critical physical aspects of tissue engineering, including tissue infiltration, biodegradation rate, and mechanical properties. Manual measurements of pore sizes from scanning electron micrographs using ImageJ/FIJI are commonly used to characterize scaffolds, but these methods are both time-consuming and subject to user bias. Current semi-automated analysis tools are limited by a lack of accessibility or limited sample size in their verification process. The work here describes the development of a new MATLAB algorithm, PoreScript, to address these limitations. The algorithm was verified using three common scaffold fabrication methods (e.g., salt leaching, gas foaming, emulsion templating) with varying pore sizes and shapes to demonstrate the versatility of this new tool. Our results demonstrate that the pore size characterization using PoreScript is comparable to manual pore size measurements. The PoreScript algorithm was further evaluated to determine the effect of user-input and image parameters (relative image magnification, pixel intensity threshold, and pore structure). Overall, this work validates the accuracy of the PoreScript algorithm across several fabrication methods and provides user-guidance for semi-automated image analysis and increased throughput of scaffold characterization.

SUBMITTER: Jenkins D

PROVIDER: S-EPMC8843970 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

PoreScript: Semi-automated pore size algorithm for scaffold characterization.

Jenkins Dana D Salhadar Karim K Ashby Grant G Mishra Anita A Cheshire Joy J Beltran Felipe F Grunlan Melissa M Andrieux Sébastien S Stubenrauch Cosima C Cosgriff-Hernandez Elizabeth E

Bioactive materials 20211112

The scaffold pore size influences many critical physical aspects of tissue engineering, including tissue infiltration, biodegradation rate, and mechanical properties. Manual measurements of pore sizes from scanning electron micrographs using ImageJ/FIJI are commonly used to characterize scaffolds, but these methods are both time-consuming and subject to user bias. Current semi-automated analysis tools are limited by a lack of accessibility or limited sample size in their verification process. Th ...[more]

PMID: 35224287

Similar Datasets

Project description:PurposeAccurate and robust auto-segmentation of highly deformable organs (HDOs), for example, stomach or bowel, remains an outstanding problem due to these organs' frequent and large anatomical variations. Yet, time-consuming manual segmentation of these organs presents a particular challenge to time-limited modern radiotherapy techniques such as on-line adaptive radiotherapy and high-dose-rate brachytherapy. We propose a machine-assisted interpolation (MAI) that uses prior information in the form of sparse manual delineations to facilitate rapid, accurate segmentation of the stomach from low field magnetic resonance images (MRI) and the bowel from computed tomography (CT) images.MethodsStomach MR images from 116 patients undergoing 0.35T MRI-guided abdominal radiotherapy and bowel CT images from 120 patients undergoing high dose rate pelvic brachytherapy treatment were collected. For each patient volume, the manual delineation of the HDO was extracted from every 8th slice. These manually drawn contours were first interpolated to obtain an initial estimate of the HDO contour. A two-channel 64 × 64 pixel patch-based convolutional neural network (CNN) was trained to localize the position of the organ's boundary on each slice within a five-pixel wide road using the image and interpolated contour estimate. This boundary prediction was then input, in conjunction with the image, to an organ closing CNN which output the final organ segmentation. A Dense-UNet architecture was used for both networks. The MAI algorithm was separately trained for the stomach segmentation and the bowel segmentation. Algorithm performance was compared against linear interpolation (LI) alone and against fully automated segmentation (FAS) using a Dense-UNet trained on the same datasets. The Dice Similarity Coefficient (DSC) and mean surface distance (MSD) metrics were used to compare the predictions from the three methods. Statistically significance was tested using Student's t test.ResultsFor the stomach segmentation, the mean DSC from MAI (0.91 ± 0.02) was 5.0% and 10.0% higher as compared to LI and FAS, respectively. The average MSD from MAI (0.77 ± 0.25 mm) was 0.54 and 3.19 mm lower compared to the two other methods. Only 7% of MAI stomach predictions resulted in a DSC < 0.8, as compared to 30% and 28% for LI and FAS, respectively. For the bowel segmentation, the mean DSC of MAI (0.90 ± 0.04) was 6% and 18% higher, and the average MSD of MAI (0.93 ± 0.48 mm) was 0.42 and 4.9 mm lower as compared to LI and FAS. Sixteen percent of the predicted contour from MAI resulted in a DSC < 0.8, as compared to 46% and 60% for FAS and LI, respectively. All comparisons between MAI and the baseline methods were found to be statistically significant (p-value < 0.001).ConclusionsThe proposed MAI algorithm significantly outperformed LI in terms of accuracy and robustness for both stomach segmentation from low-field MRIs and bowel segmentation from CT images. At this time, FAS methods for HDOs still require significant manual editing. Therefore, we believe that the MAI algorithm has the potential to expedite the process of HDO delineation within the radiation therapy workflow.

Project description:Purpose: To assess the accuracy and efficacy of a semi-automated deep learning algorithm (DLA) assisted approach to detect vision-threatening diabetic retinopathy (DR). Methods: We developed a two-step semi-automated DLA-assisted approach to grade fundus photographs for vision-threatening referable DR. Study images were obtained from the Lingtou Cohort Study, and captured at participant enrollment in 2009-2010 ("baseline images") and annual follow-up between 2011 and 2017. To begin, a validated DLA automatically graded baseline images for referable DR and classified them as positive, negative, or ungradable. Following, each positive image, all other available images from patients who had a positive image, and a 5% random sample of all negative images were selected and regraded by trained human graders. A reference standard diagnosis was assigned once all graders achieved consistent grading outcomes or with a senior ophthalmologist's final diagnosis. The semi-automated DLA assisted approach combined initial DLA screening and subsequent human grading for images identified as high-risk. This approach was further validated within the follow-up image datasets and its time and economic costs evaluated against fully human grading. Results: For evaluation of baseline images, a total of 33,115 images were included and automatically graded by the DLA. 2,604 images (480 positive results, 624 available other images from participants with a positive result, and 1500 random negative samples) were selected and regraded by graders. The DLA achieved an area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.953, 0.970, 0.879, and 88.6%, respectively. In further validation within the follow-up image datasets, a total of 88,363 images were graded using this semi-automated approach and human grading was performed on 8975 selected images. The DLA achieved an AUC, sensitivity, and specificity of 0.914, 0.852, 0.853, respectively. Compared against fully human grading, the semi-automated DLA-assisted approach achieved an estimated 75.6% time and 90.1% economic cost saving. Conclusions: The DLA described in this study was able to achieve high accuracy, sensitivity, and specificity in grading fundus images for referable DR. Validated against long-term follow-up datasets, a semi-automated DLA-assisted approach was able to accurately identify suspect cases, and minimize misdiagnosis whilst balancing safety, time, and economic cost.

Dataset Information

PoreScript: Semi-automated pore size algorithm for scaffold characterization.

Publications

PoreScript: Semi-automated pore size algorithm for scaffold characterization.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets