  Acute appendicitis characterized by a painful inflammation of the vermiform
appendix is one of the most common surgical emergencies. Localizing the
appendix is challenging due to its unclear anatomy amidst the complex
colon-structure as observed in the conventional CT views, resulting in a
time-consuming diagnosis. End-to-end learning of a convolutional neural network
(CNN) is also not likely to be useful because of the negligible size of the
appendix compared with the abdominal CT volume. With no prior computational
approaches to the best of our knowledge, we propose the first computerized
automation for acute appendicitis diagnosis. In our approach, we utilize a
reinforcement learning agent deployed in the lower abdominal region to obtain
the appendix location first to reduce the search space for diagnosis. Then, we
obtain the classification scores (i.e., the likelihood of acute appendicitis)
for the local neighborhood around the localized position, using a CNN trained
only on a small appendix patch per volume. From the spatial representation of
the resultant scores, we finally define a region of low-entropy (RLE) to choose
the optimal diagnosis score, which helps improve the classification accuracy
showing robustness even under high appendix localization error cases. In our
experiment with 319 abdominal CT volumes, the proposed RLE-based decision with
prior localization showed significant improvement over the standard CNN-based
diagnosis approaches.
