Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images

Nunez-Iglesias, Juan; Kennedy, Ryan; Parag, Toufiq; Shi, Jianbo; Chklovskii, Dmitri B.

doi:10.1371/journal.pone.0071715

Cited by 126 publications

(167 citation statements)

References 33 publications

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“…The problem is solved using multicut integer linear programming. Another recent work [10] which also uses an initial watershed segmentation and performs agglomeration, learns how to merge watershed segments via active learning methods.…”

Section: Introductionmentioning

confidence: 99%

Optree: A Learning-Based Adaptive Watershed Algorithm for Neuron Segmentation

Uzunbaş

Chen

Metaxas

2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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Abstract. We present a new algorithm for automatic and interactive segmentation of neuron structures from electron microscopy (EM) images. Our method selects a collection of nodes from the watershed merging tree as the proposed segmentation. This is achieved by building a conditional random field (CRF) whose underlying graph is the merging tree. The maximum a posteriori (MAP) prediction of the CRF is the output segmentation. Our algorithm outperforms state-of-the-art methods. Both the inference and the training are very efficient as the graph is tree-structured. Furthermore, we develop an interactive segmentation framework which selects uncertain regions for a user to proofread. The uncertainty is measured by the marginals of the graphical model. Based on user corrections, our framework modifies the merging tree and thus improves the segmentation globally.

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Section: Introductionmentioning

confidence: 99%

Optree: A Learning-Based Adaptive Watershed Algorithm for Neuron Segmentation

Uzunbaş

Chen

Metaxas

2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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“…Importantly, the speed of our pipeline does not come at the expense of accuracy, which is on par or better than existing systems in the literature [29,56,58] (using the accepted variation of information (VI) measure [44]). Our high-level pipeline design builds on prior work [29,42,51,52,55,56]. It passes the data through several stages as seen in Figure 2.…”

Section: High-throughput Connectomicsmentioning

confidence: 99%

A Multicore Path to Connectomics-on-Demand

Shavit

2016

Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures

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The current design trend in large scale machine learning is to use distributed clusters of CPUs and GPUs with MapReduce-style programming. Some have been led to believe that this type of horizontal scaling can reduce or even eliminate the need for traditional algorithm development, careful parallelization, and performance engineering. This paper is a case study showing the contrary: that the benefits of algorithms, parallelization, and performance engineering, can sometimes be so vast that it is possible to solve "clusterscale" problems on a single commodity multicore machine.Connectomics is an emerging area of neurobiology that uses cutting edge machine learning and image processing to extract brain connectivity graphs from electron microscopy images. It has long been assumed that the processing of connectomics data will require mass storage, farms of CPU/GPUs, and will take months (if not years) of processing time. We present a high-throughput connectomics-ondemand system that runs on a multicore machine with less than 100 cores and extracts connectomes at the terabyte per hour pace of modern electron microscopes.

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“…Most closely related to our work are previous methods for learning affinity functions for hierarchical clustering [21]. For example, Jain et al [13] train a convolutional neural network to directly minimize the RAND error, a measure of segmentation quality, on segmentations of neuron images.…”

Section: Related Workmentioning

confidence: 99%

Learning Hierarchical Semantic Segmentations of LIDAR Data

Dohan

Matejek

Funkhouser

2015

2015 International Conference on 3D Vision

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This paper investigates a method for semantic segmentation of small objects in terrestrial LIDAR scans in urban environments. The core research contribution is a hierarchical segmentation algorithm where potential merges between segments are prioritized by a learned affinity function and constrained to occur only if they achieve a significantly high object classification probability. This approach provides a way to integrate a learned shape-prior (the object classifier) into a search for the best semantic segmentation in a fast and practical algorithm. Experiments with LIDAR scans collected by Google Street View cars throughout ∼100 city blocks of New York City show that the algorithm provides better segmentations and classifications than simple alternatives for cars, vans, traffic lights, and street lights.

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Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images

Cited by 126 publications

References 33 publications

Optree: A Learning-Based Adaptive Watershed Algorithm for Neuron Segmentation

Optree: A Learning-Based Adaptive Watershed Algorithm for Neuron Segmentation

A Multicore Path to Connectomics-on-Demand

Learning Hierarchical Semantic Segmentations of LIDAR Data

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