  Quantitative bone single-photon emission computed tomography (QBSPECT) has
the potential to provide a better quantitative assessment of bone metastasis
than planar bone scintigraphy due to its ability to better quantify activity in
overlapping structures. An important element of assessing response of bone
metastasis is accurate image segmentation. However, limited by the properties
of QBSPECT images, the segmentation of anatomical regions-of-interests (ROIs)
still relies heavily on the manual delineation by experts. This work proposes a
fast and robust automated segmentation method for partitioning a QBSPECT image
into lesion, bone, and background. We present a new unsupervised segmentation
loss function and its semi- and supervised variants for training a
convolutional neural network (ConvNet). The loss functions were developed based
on the objective function of the classical Fuzzy C-means (FCM) algorithm. We
conducted a comprehensive study to compare our proposed methods with ConvNets
trained using supervised loss functions and conventional clustering methods.
The Dice similarity coefficient (DSC) and several other metrics were used as
figures of merit as applied to the task of delineating lesion and bone in both
simulated and clinical SPECT/CT images. We experimentally demonstrated that the
proposed methods yielded good segmentation results on a clinical dataset even
though the training was done using realistic simulated images. A ConvNet-based
image segmentation method that uses novel loss functions was developed and
evaluated. The method can operate in unsupervised, semi-supervised, or
fully-supervised modes depending on the availability of annotated training
data. The results demonstrated that the proposed method provides fast and
robust lesion and bone segmentation for QBSPECT/CT. The method can potentially
be applied to other medical image segmentation applications.
