Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol.

ABSTRACT:

Background

To study deep learning segmentation of knee anatomy with 13 anatomical classes by using a magnetic resonance (MR) protocol of four three-dimensional (3D) pulse sequences, and evaluate possible clinical usefulness.

Methods

The sample selection involved 40 healthy right knee volumes from adult participants. Further, a recently injured single left knee with previous known ACL reconstruction was included as a test subject. The MR protocol consisted of the following 3D pulse sequences: T1 TSE, PD TSE, PD FS TSE, and Angio GE. The DenseVNet neural network was considered for these experiments. Five input combinations of sequences (i) T1, (ii) T1 and FS, (iii) PD and FS, (iv) T1, PD, and FS and (v) T1, PD, FS and Angio were trained using the deep learning algorithm. The D

SUBMITTER: Kulseng CPS 

PROVIDER: S-EPMC9847207 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature