A transfer learning approach for automatic segmentation of the surgically treated anterior cruciate ligament

Quantitative magnetic resonance imaging enables quantitative assessment of the healing anterior cruciate ligament or graft post-surgery, but its use is constrained by the need for time consuming manual image segmentation. The goal of this study was to validate a deep learning model for automatic segmentation of repaired and reconstructed anterior cruciate ligaments. We hypothesized that (1) a deep learning model would segment repaired ligaments and grafts with comparable anatomical similarity to intact ligaments, and (2) automatically derived quantitative features (i.e., signal intensity and volume) would not be significantly different from those obtained by manual segmentation. Constructive Interference in Steady State sequences were acquired of ACL repairs (n = 238) and grafts (n = 120). A previously validated model for intact ACLs was retrained on both surgical groups using transfer learning. Anatomical performance was measured with Dice coefficient, sensitivity, and precision. Quantitative features were compared to ground truth manual segmentation. Automatic segmentation of both surgical groups resulted in decreased anatomical performance compared to intact ACL automatic segmentation (repairs/grafts: Dice coefficient = .80/.78, precision = .79/.78, sensitivity = .82/.80), but neither decrease was statistically significant (Kruskal-Wallis: Dice coefficient p = .02, precision p = .09, sensitivity p = .17; Dunn post-hoc test for Dice coefficient: repairs/grafts p = .054/.051). There were no significant differences in quantitative features between the ground truth and automatic segmentation of repairs/grafts (0.82/2.7% signal intensity difference, p = .57/.26; 1.7/2.7% volume difference, p = .68/.72). The anatomical similarity performance and statistical similarities of quantitative features supports the use of this automated segmentation model in quantitative magnetic resonance imaging pipelines, which will accelerate research and provide a step towards clinical applicability.

Automated magnetic resonance image segmentation of the anterior cruciate ligament

The objective of this study was to develop an automated segmentation method for the anterior cruciate ligament that is capable of facilitating quantitative assessments of the ligament in clinical and research settings. A modified U-Net fully convolutional network model was trained, validated, and tested on 246 Constructive Interference in Steady State magnetic resonance images of intact anterior cruciate ligaments. Overall model performance was assessed on the image set relative to an experienced (>5 years) "ground truth" segmenter in two domains: anatomical similarity and the accuracy of quantitative measurements (i.e., signal intensity and volume) obtained from the automated segmentation. To establish model reliability relative to manual segmentation, a subset of the imaging data was resegmented by the ground truth segmenter and two additional segmenters (A, 6 months and B, 2 years of experience), with their performance evaluated relative to the ground truth. The final model scored well on anatomical performance metrics (Dice coefficient = 0.84, precision = 0.82, and sensitivity = 0.85). The median signal intensities and volumes of the automated segmentations were not significantly different from ground truth (0.3% difference, p = .9; 2.3% difference, p = .08, respectively). When the model results were compared with the independent segmenters, the model predictions demonstrated greater median Dice coefficient (A = 0.73, p = .001; B = 0.77, p = NS) and sensitivity (A = 0.68, p = .001; B = 0.72, p = .003). The model performed equivalently well to retest segmentation by the ground truth segmenter on all measures. The quantitative measures extracted from the automated segmentation model did not differ from those of manual segmentation, enabling their use in quantitative magnetic resonance imaging pipelines to evaluate the anterior cruciate ligament.

Seasonal Influenza Vaccine: Uptake, Attitude, and Knowledge Among Patients Receiving Immunoglobulin Replacement Therapy

Influenza is a potential cause of severe disease in the immunocompromised. Patients with hypogammaglobulinemia, in spite of adequate replacement therapy, are at risk of significant morbidity and adverse outcomes. A seasonal vaccine is the primary prophylactic countermeasure to limit disease. The aim of this study was to evaluate the attitude, knowledge, and influenza vaccine uptake among Irish patients receiving immunoglobulin replacement therapy (IgRT), as well as uptake in co-habitants. Fifty-seven percent of patients receiving IgRT at a regional immunology referral center completed a questionnaire evaluation. Seventy-six percent of IgRT patients received the influenza vaccine for the 2019 season. Ninety-eight percent recognized that influenza could be prevented with vaccination, and 81% deemed it a safe treatment. Ninety-three percent correctly identified that having a chronic medical condition, independent of age, was an indication for vaccination. Despite excellent compliance and knowledge, many were not aware that vaccination was recommended for co-habitants, and only 24% had full vaccine coverage at home. Those who received advice regarding vaccination of household members had higher rates of uptake at home. This study demonstrates awareness and adherence to seasonal influenza vaccine recommendations among patients receiving IgRT. Over three quarters felt adequately informed, the majority stating physicians as their information source. We identified an easily modifiable knowledge gap regarding vaccination of household members. This data reveals a need to emphasize the importance of vaccination for close contacts of at-risk patients, to maintain optimal immunity and health outcome.