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. Author manuscript; available in PMC: 2018 May 4.
Published in final edited form as: Comput Methods Biomech Biomed Eng Imaging Vis. 2016 Apr 28;6(3):270–276. doi: 10.1080/21681163.2016.1141063

Figure 3.

Figure 3

Training label creation. The original image (a) only has (b) a select few of its nuclei annotated, leaving it difficult to find patches which create a challenging negative class. Our approach is to create (c) a dilated edge mask. Sampling locations from (c) allows us to create negative class samples which are of very high utility for the DL algorithm. As a result, our improved patch selection technique leads to (e) notably better delineated nuclei boundaries as compared to (d) a typical approach.