  Precise segmentation of knee tissues from magnetic resonance imaging (MRI) is
critical in quantitative imaging and diagnosis. Convolutional neural networks
(CNNs), which are state of the art, have limitations owing to the lack of
image-specific adaptation, such as low tissue contrasts and structural
inhomogeneities, thereby leading to incomplete segmentation results. This paper
presents a deep learning based automatic segmentation framework for knee tissue
segmentation. A novel multipath CNN-based method is proposed, which consists of
an encoder decoder-based segmentation network in combination with a low rank
tensor-reconstructed segmentation network. Low rank reconstruction in MRI
tensor sub-blocks is introduced to exploit the structural and morphological
variations in knee tissues. To further improve the segmentation from CNNs,
trimap generation, which effectively utilizes superimposed regions, is proposed
for defining high, medium and low confidence regions from the multipath CNNs.
The secondary path with low rank reconstructed input mitigates the conditions
in which the primary segmentation network can potentially fail and overlook the
boundary regions. The outcome of the segmentation is solved as an alpha matting
problem by blending the trimap with the source input. Experiments on
Osteoarthritis Initiative (OAI) datasets and a self prepared scan validate the
effectiveness of the proposed method. We specifically demonstrate the
application of the proposed method in a cartilage segmentation based thickness
map for diagnosis purposes.
