Abstract
Knee-related injuries, including meniscal tears, are common in young athletes and require accurate diagnosis and
appropriate surgical intervention. Although with proper technique and skill, confidence in the detection of meniscal
tears should be high, this task continues to be a challenge for many inexperienced radiologists. The purpose of our study
was to automate detection of meniscal tears of the knee using a computer-aided detection (CAD) algorithm. Automated
segmentation of the sagittal T1-weighted MR imaging sequences of the knee in 28 patients with diagnoses of meniscal
tears was performed using morphologic image processing in a 3-step process including cropping, thresholding, and
application of morphological constraints. After meniscal segmentation, abnormal linear meniscal signal was extracted
through a second thresholding process. The results of this process were validated by comparison with the interpretations
of 2 board-certified musculoskeletal radiologists. The automated meniscal extraction algorithm process was able to
successfully perform region of interest selection, thresholding, and object shape constraint tasks to produce a convex
image isolating the menisci in more than 69% of the 28 cases. A high correlation was also noted between the CAD
algorithm and human observer results in identification of complex meniscal tears. Our initial investigation indicates
considerable promise for automatic detection of simple and complex meniscal tears of the knee using the CAD
algorithm. This observation poses interesting possibilities for increasing radiologist productivity and confidence,
improving patient outcomes, and applying more sophisticated CAD algorithms to orthopedic imaging tasks.
