Combinatorial Energy Learning for Image Segmentation
This work addresses image segmentation, particularly for 3-D microscopy data, offering a novel approach that yields significant accuracy gains, though it appears incremental in its application to a specific domain.
The paper tackles the problem of image segmentation by introducing a neural network to model the conditional energy of segmentations, emphasizing combinatorial aspects. On a 3-D microscopy dataset with an 11 billion voxel test set, the method improves volumetric reconstruction accuracy by over 20% compared to state-of-the-art baselines.
We introduce a new machine learning approach for image segmentation that uses a neural network to model the conditional energy of a segmentation given an image. Our approach, combinatorial energy learning for image segmentation (CELIS) places a particular emphasis on modeling the inherent combinatorial nature of dense image segmentation problems. We propose efficient algorithms for learning deep neural networks to model the energy function, and for local optimization of this energy in the space of supervoxel agglomerations. We extensively evaluate our method on a publicly available 3-D microscopy dataset with 25 billion voxels of ground truth data. On an 11 billion voxel test set, we find that our method improves volumetric reconstruction accuracy by more than 20% as compared to two state-of-the-art baseline methods: graph-based segmentation of the output of a 3-D convolutional neural network trained to predict boundaries, as well as a random forest classifier trained to agglomerate supervoxels that were generated by a 3-D convolutional neural network.