  Knee arthroscopy is a minimally invasive surgical (MIS) procedure which is
performed to treat knee-joint ailment. Lack of visual information of the
surgical site obtained from miniaturized cameras make this surgical procedure
more complex. Knee cavity is a very confined space; therefore, surgical scenes
are captured at close proximity. Insignificant context of knee atlas often
makes them unrecognizable as a consequence unintentional tissue damage often
occurred and shows a long learning curve to train new surgeons. Automatic
context awareness through labeling of the surgical site can be an alternative
to mitigate these drawbacks. However, from the previous studies, it is
confirmed that the surgical site exhibits several limitations, among others,
lack of discriminative contextual information such as texture and features
which drastically limits this vision task. Additionally, poor imaging
conditions and lack of accurate ground-truth labels are also limiting the
accuracy. To mitigate these limitations of knee arthroscopy, in this work we
proposed a scene segmentation method that successfully segments multi
structures.
