  Convolutional neural network (CNN) methods have been proposed to quantify
lesions in medical imaging. Commonly more than one imaging examination is
available for a patient, but the serial information in these images often
remains unused. CNN-based methods have the potential to extract valuable
information from previously acquired imaging to better quantify current imaging
of the same patient. A pre-trained CNN can be updated with a patient's
previously acquired imaging: patient-specific fine-tuning. In this work, we
studied the improvement in performance of lesion quantification methods on MR
images after fine-tuning compared to a base CNN. We applied the method to two
different approaches: the detection of liver metastases and the segmentation of
brain white matter hyperintensities (WMH). The patient-specific fine-tuned CNN
has a better performance than the base CNN. For the liver metastases, the
median true positive rate increases from 0.67 to 0.85. For the WMH
segmentation, the mean Dice similarity coefficient increases from 0.82 to 0.87.
In this study we showed that patient-specific fine-tuning has potential to
improve the lesion quantification performance of general CNNs by exploiting the
patient's previously acquired imaging.
