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Image-based sequencing of mRNA makes it possible to see where in a tissuesample a given gene is active, and thus discern large numbers of different celltypes in parallel. This is crucial for gaining a better understanding of tissuedevelopment and disease such as cancer. Signals are collected over multiplestaining and imaging cycles, and signal density together with noise makessignal decoding challenging. Previous approaches have led to low signal recallin efforts to maintain high sensitivity. We propose an approach where signalcandidates are generously included, and true-signal probability at the cyclelevel is self-learned using a convolutional neural network. Signal candidatesand probability predictions are thereafter fed into a graphical model searchingfor signal candidates across sequencing cycles. The graphical model combinesintensity, probability and spatial distance to find optimal paths representingdecoded signal sequences. We evaluate our approach in relation tostate-of-the-art, and show that we increase recall by $27\%$ at maintainedsensitivity. Furthermore, visual examination shows that most of the nowcorrectly resolved signals were previously lost due to high signal density.Thus, the proposed approach has the potential to significantly improve furtheranalysis of spatial statistics in in situ sequencing experiments.

Improving Recall of In Situ Sequencing by Self-Learned Features and a Graphical Model