+The input for Pythostitcher consists of either two or four high resolution histopathology images and the corresponding tissue masks. These must both be pyramidal files and consist of incrementally downsampled resolutions (preferable .mrxs or .tif format). These tissue masks can be generated by your tissue segmentation algorithm of choice, in the provided sample data we make use of the algorithm from [Bándi et al](https://pubmed.ncbi.nlm.nih.gov/31871843/). Although not required, the user may specify the desired location of the tissue fragments in the final reconstruction. In the case of a prostatectomy cross-section which consists of two fragments, these locations would usually be left and right. Currently, supported locations are ['right', 'left', 'top', 'bottom']. If these locations are not specified, PythoStitcher will automatically figure out the most suitable way to reconstruct the whole-mount. This will be performed by using [JigsawNet](https://github.com/Lecanyu/JigsawNet), a CNN trained to identify adjacent fragments in jigsaw puzzles.
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