Deep Learning-Based Automated Segmentation and Detection of Chondral Lesions on the Distal Femur

Sammanfattning: Articular chondral lesions in the knee joint can be diagnosed at an early stage using MRI. Segmenting and visualizing lesions and the overall joint structure allows improved communication between the radiologist and referring physician. It can also be of help when determining diagnosis or conducting surgical planning. Although there are a variety of studies proving good results of segmentation of larger structures such as bone and cartilage in the knee, there are no studies available researching segmentation of articular cartilage lesions. Automating the segmentation will save time and money since manual segmentation is very time-consuming. In this thesis, a U-Net based convolutional neural network is used to perform automatic segmentation of chondral lesions located on the distal part of the femur, in the knee joint. Using two different techniques, batch normalization and dropout, a network was trained and tested using MRI sequences collected from Episurf Medical's database. The network was then evaluated using a segmentation approach and a detection approach. For the segmentation approach, the highest achieved dice coefficient and sensitivity of 0.4059 ± 0.1833 and 0.4591 ± 0.2387, was obtained using batch normalization and 260 training subjects, consisting of MRI sequence and corresponding masks. Using a detection approach, the predicted output could correctly identify 81.8% of the chondral lesions in the MRI sequences. Although there is a need for improvement of technique and datasets used in this thesis, the achieved results show prerequisites for future improvement and possible implementation.