Segmentation of tibiofemoral joint tissues from knee MRI using MtRA-Unet
  and incorporating shape information: Data from the Osteoarthritis Initiative
Akshay Daydar Alik Pramanick Arijit Sur Subramani Kanagaraj
Knee Osteoarthritis (KOA) is the third most prevalent Musculoskeletal
Disorder (MSD) after neck and back pain. To monitor such a severe MSD, a
segmentation map of the femur, tibia and tibiofemoral cartilage is usually
accessed using the automated segmentation algorithm from the Magnetic Resonance
Imaging (MRI) of the knee. But, in recent works, such segmentation is
conceivable only from the multistage framework thus creating data handling
issues and needing continuous manual inference rendering it unable to make a
quick and precise clinical diagnosis. In order to solve these issues, in this
paper the Multi-Resolution Attentive-Unet (MtRA-Unet) is proposed to segment
the femur, tibia and tibiofemoral cartilage automatically. The proposed work
has included a novel Multi-Resolution Feature Fusion (MRFF) and Shape
Reconstruction (SR) loss that focuses on multi-contextual information and
structural anatomical details of the femur, tibia and tibiofemoral cartilage.
Unlike previous approaches, the proposed work is a single-stage and end-to-end
framework producing a Dice Similarity Coefficient (DSC) of 98.5% for the femur,
98.4% for the tibia, 89.1% for Femoral Cartilage (FC) and 86.1% for Tibial
Cartilage (TC) for critical MRI slices that can be helpful to clinicians for
KOA grading. The time to segment MRI volume (160 slices) per subject is 22 sec.
which is one of the fastest among state-of-the-art. Moreover, comprehensive
experimentation on the segmentation of FC and TC which is of utmost importance
for morphology-based studies to check KOA progression reveals that the proposed
method has produced an excellent result with binary segmentation