  Calcaneus is the largest tarsal bone to withstand the daily stresses of
weight-bearing. The calcaneal fracture is the most common type in the tarsal
bone fractures. After a fracture is suspected, plain radiographs should be
taken first. Bohler's Angle (BA) and Critical Angle of Gissane (CAG), measured
by four anatomic landmarks in lateral foot radiograph, can guide fracture
diagnosis and facilitate operative recovery of the fractured calcaneus. This
study aims to develop an analysis system that can automatically locate four
anatomic landmarks, measure BA and CAG for fracture assessment, identify
fractured calcaneus, and segment fractured regions. For landmark detection, we
proposed a coarse-to-fine Rotation-Invariant Regression-Voting (RIRV) landmark
detection method based on regressive Multi-Layer Perceptron (MLP) and Scale
Invariant Feature Transform (SIFT) patch descriptor, which solves the problem
of fickle rotation of calcaneus. By implementing a novel normalization
approach, the RIRV method is explicitly rotation-invariance comparing with
traditional regressive methods. For fracture identification and segmentation, a
convolution neural network (CNN) based on U-Net with auxiliary classification
head (U-Net-CH) is designed. The input ROIs of the CNN are normalized by
detected landmarks to uniform view, orientation, and scale. The advantage of
this approach is the multi-task learning that combines classification and
segmentation. Our system can accurately measure BA and CAG with a mean angle
error of 3.8 and 6.2 respectively. For fracture identification and fracture
region segmentation, our system presents good performance with an F1-score of
96.55%, recall of 94.99%, and segmentation IoU-score of 0.586.
