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Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images
| Content Provider | SAGE Publishing |
|---|---|
| Author | Harkey, Matthew S. Michel, Nicholas Kuenze, Christopher Fajardo, Ryan Salzler, Matt Driban, Jeffrey B. Hacihaliloglu, Ilker |
| Copyright Year | 2022 |
| Abstract | ObjectiveTo validate a semi-automated technique to segment ultrasound-assessed femoral cartilage without compromising segmentation accuracy to a traditional manual segmentation technique in participants with an anterior cruciate ligament injury (ACL).DesignWe recruited 27 participants with a primary unilateral ACL injury at a pre-operative clinic visit. One investigator performed a transverse suprapatellar ultrasound scan with the participant’s ACL injured knee in maximum flexion. Three femoral cartilage ultrasound images were recorded. A single expert reader manually segmented the femoral cartilage cross-sectional area in each image. In addition, we created a semi-automatic program to segment the cartilage using a random walker-based method. We quantified the average cartilage thickness and echo-intensity for the manual and semi-automated segmentations. Intraclass correlation coefficients (ICC2,k) and Bland-Altman plots were used to validate the semi-automated technique to the manual segmentation for assessing average cartilage thickness and echo-intensity. A dice correlation coefficient was used to quantify the overlap between the segmentations created with the semi-automated and manual techniques.ResultsFor average cartilage thickness, there was excellent reliability (ICC2,k = 0.99) and a small mean difference (+0.8%) between the manual and semi-automated segmentations. For average echo-intensity, there was excellent reliability (ICC2,k = 0.97) and a small mean difference (−2.5%) between the manual and semi-automated segmentations. The average dice correlation coefficient between the manual segmentation and semi-automated segmentation was 0.90, indicating high overlap between techniques.ConclusionsOur novel semi-automated segmentation technique is a valid method that requires less technical expertise and time than manual segmentation in patients after ACL injury. |
| Related Links | https://journals.sagepub.com/doi/pdf/10.1177/19476035221093069?download=true |
| ISSN | 19476035 |
| Issue Number | 2 |
| Volume Number | 13 |
| Journal | CARTILAGE (CAR) |
| e-ISSN | 19476043 |
| DOI | 10.1177/19476035221093069 |
| Language | English |
| Publisher | Sage Publications CA |
| Publisher Date | 2022-04-19 |
| Publisher Place | Los Angeles |
| Access Restriction | Open |
| Rights Holder | © The Author(s) 2022 |
| Subject Keyword | anterior cruciate ligament injury cartilage thickness diagnostic ultrasound trochlea |
| Content Type | Text |
| Resource Type | Article |
| Subject | Immunology and Allergy Physical Therapy, Sports Therapy and Rehabilitation Biomedical Engineering |
