  Image-based sequencing of mRNA makes it possible to see where in a tissue
sample a given gene is active, and thus discern large numbers of different cell
types in parallel. This is crucial for gaining a better understanding of tissue
development and disease such as cancer. Signals are collected over multiple
staining and imaging cycles, and signal density together with noise makes
signal decoding challenging. Previous approaches have led to low signal recall
in efforts to maintain high sensitivity. We propose an approach where signal
candidates are generously included, and true-signal probability at the cycle
level is self-learned using a convolutional neural network. Signal candidates
and probability predictions are thereafter fed into a graphical model searching
for signal candidates across sequencing cycles. The graphical model combines
intensity, probability and spatial distance to find optimal paths representing
decoded signal sequences. We evaluate our approach in relation to
state-of-the-art, and show that we increase recall by $27\%$ at maintained
sensitivity. Furthermore, visual examination shows that most of the now
correctly resolved signals were previously lost due to high signal density.
Thus, the proposed approach has the potential to significantly improve further
analysis of spatial statistics in in situ sequencing experiments.
