ARID

Abstract

Instance segmentation algorithms are used everywhere, be it self driving cars, scene mapping by autonomous robots or analyzing medical scans. Instance segmentation can be thought of as further refinement of semantic segmentation. Object detection algorithms try to detect objects from the scene by enclosing them in bounding boxes, semantic segmentation tries to label these objects, whereas instance segmentation tries to label each unique instance of these objects. The task is quite complex and becomes even more challenging when the scope is microscopic data. Objects in microscopic data do not usually follow a fixed shape or orientation, therefore it becomes very difficult to identify unique instances of these objects using axis aligned bounding boxes. The alternative approach that researchers take is to do pixel wise prediction and then agglomerate those together to ultimately get the final object instances. In this thesis we presented a novel loss function which we have used to train a U-Net which predicts n-dimensional embedding maps or ARID(Affinity Representing Instance Descriptors). These embedding vectors contain dense information which can then be used to generate segmentation maps using the post processing approaches. Previous methods have attempted to learn affinities but are prone to errors resulting in erroneous segmentation. We show that our segmentation pipeline using ARID embedding map surpasses the performance of the affinity based networks and solve the problem of merge errors. Our segmentation pipeline have two phases, first one is predicting ARID embedding for which we have trained U-Net architecture using ultrametric loss. Multiple configurations were tested and compared. Second phase is post processing. Post processing is further divided in two steps segmentation generation and refinement. We presented a very basic technique to generate a euclidean minimum spanning tree and prune the edges with distance bigger than the provided threshold to generate segmentation. The other part of the post processing pipeline is segmentation refinement. Where we proposed approaches to refine the generated segmentation. We have used IOU scores under thresholds of Average Precision(AP) raging from 0.5 to 0.95 with an increment of 0.05 to evaluate the performance. The best average AP0.5 IOU score that we got from the affinity based networks is 0.63, we have shown that our segmentation pipeline generates the segmentation maps which gives the best average performance of 0.826 AP0.5 IOU score, surpassing the affinity based network performance. We have also shown the failure modes of our proposed loss function and presented future scope of research in the field. Embedding based approaches show promise to do efficient instance segmentation especially in complex scenes as is in the microscopic data. The generalized loss function that we have presented in this thesis is capable of doing this task, and presents a better alternative to using affinity based methods to do segmentation.--Author's abstract