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  Indian J Med Microbiol
 

Figure 3: The process of creation of training exemplars to enhance the result obtained via deep learning for nuclei segmentation. The original image (a) only has (b) a select few of its nuclei annotated. This makes it difficult to find patches which represent a challenging negative class. Our approach involves augmenting a basic negative class, created by sampling from the thresholded color deconvoluted image. More challenging patches are supplied by (c) a dilated edge mask. Sampling locations from (c) allows us to create negative class samples which are of very high utility for the deep learning algorithm. As a result, our improved patch selection technique leads to (e) notably better-delineated nuclei boundaries as compared to the approach shown in (d)

Figure 3: The process of creation of training exemplars to enhance the result obtained via deep learning for nuclei segmentation. The original image (a) only has (b) a select few of its nuclei annotated. This makes it difficult to find patches which represent a challenging negative class. Our approach involves augmenting a basic negative class, created by sampling from the thresholded color deconvoluted image. More challenging patches are supplied by (c) a dilated edge mask. Sampling locations from (c) allows us to create negative class samples which are of very high utility for the deep learning algorithm. As a result, our improved patch selection technique leads to (e) notably better-delineated nuclei boundaries as compared to the approach shown in (d)