Domain adaptive segmentation in volume electron microscopy imaging

In the last years, automated segmentation has become a necessary tool for volume electron microscopy (EM) imaging. So far, the best performing techniques have been largely based on fully supervised encoder-decoder CNNs, requiring a substantial amount of annotated images. Domain Adaptation (DA) aims to alleviate the annotation burden by 'adapting' the networks trained on existing groundtruth data (source domain) to work on a different (target) domain with as little additional annotation as possible. Most DA research is focused on the classification task, whereas volume EM segmentation remains rather unexplored. In this work, we extend recently proposed classification DA techniques to an encoder-decoder layout and propose a novel method that adds a reconstruction decoder to the classical encoder-decoder segmentation in order to align source and target encoder features. The method has been validated on the task of segmenting mitochondria in EM volumes. We have performed DA from brain EM images to HeLa cells and from isotropic FIB/SEM volumes to anisotropic TEM volumes. In all cases, the proposed method has outperformed the extended classification DA techniques and the finetuning baseline. An implementation of our work can be found on https://github.com/JorisRoels/domain-adaptive-segmentation

Automated Analysis of Biomedical Data from Low to High Resolution

Recent developments of experimental techniques and instrumentation allow life scientists to acquire enormous volumes of data at unprecedented resolution. While this new data brings much deeper insight into cellular processes, it renders manual analysis infeasible and calls for the development of new, automated analysis procedures. This thesis describes how methods of pattern recognition can be used to automate three popular data analysis protocols: Chapter 1 proposes a method to automatically locate bimodal isotope distribution patterns in Hydrogen Deuterium Exchange Mass Spectrometry experiments. The method is based on L1-regularized linear regression and allows for easy quantitative analysis of co-populations with different exchange behavior. The sensitivity of the method is tested on a set of manually identified peptides, while its applicability to exploratory data analysis is validated by targeted follow-up peptide identification. Chapter 2 develops a technique to automate peptide quantification for mass spectrometry experiments, based on 16O/18O labeling of peptides. Two different spectrum segmentation algorithms are proposed: one based on image processing and applicable to low resolution data and one exploiting the sparsity of high resolution data. The quantification accuracy is validated on calibration datasets, produced by mixing a set of proteins in pre-defined ratios. Chapter 3 provides a method for automated detection and segmentation of synapses in electron microscopy images of neural tissue. For images acquired by scanning electron microscopy with nearly isotropic resolution, the algorithm is based on geometric features computed in 3D pixel neighborhoods. For transmission electron microscopy images with poor z-resolution, the algorithm uses additional regularization by performing several rounds of pixel classification with features computed on the probability maps of the previous classification round. The validation is performed by comparing the set of synapses detected by the algorithm against a gold standard detection by human experts. For data with nearly isotropic resolution, the algorithm performance is comparable to that of the human experts

ROOT Statistical Software

Advanced mathematical and statistical computational methods are required by the LHC experiments for analyzing their data. Some of these methods are provided by the ROOT project, a C++ Object Oriented framework for large scale data handling applications. We review the current mathematical and statistical classes present in ROOT, emphasizing the recent developments

Domain Adaptive Segmentation in Volume Electron Microscopy Imaging

In the last years, automated segmentation has become a necessary tool for volume electron microscopy (EM) imaging. So far, the best performing techniques have been largely based on fully supervised encoder-decoder CNNs, requiring a substantial amount of annotated images. Domain Adaptation (DA) aims to alleviate the annotation burden by 'adapting' the networks trained on existing groundtruth data (source domain) to work on a different (target) domain with as little additional annotation as possible. Most DA research is focused on the classification task, whereas volume EM segmentation remains rather unexplored. In this work, we extend recently proposed classification DA techniques to an encoder-decoder layout and propose a novel method that adds a reconstruction decoder to the classical encoder-decoder segmentation in order to align source and target encoder features. The method has been validated on the task of segmenting mitochondria in EM volumes. We have performed DA from brain EM images to HeLa cells and from isotropic FIB/SEM volumes to anisotropic TEM volumes. In all cases, the proposed method has outperformed the extended classification DA techniques and the finetuning baseline. An implementation of our work can be found on https://github.com/JorisRoels/domain-adaptive-segmentation.Comment: ISBI 2019 (accepted

Stateless actor-critic for instance segmentation with high-level priors

Instance segmentation is an important computer vision problem which remains challenging despite impressive recent advances due to deep learning-based methods. Given sufficient training data, fully supervised methods can yield excellent performance, but annotation of ground-truth data remains a major bottleneck, especially for biomedical applications where it has to be performed by domain experts. The amount of labels required can be drastically reduced by using rules derived from prior knowledge to guide the segmentation. However, these rules are in general not differentiable and thus cannot be used with existing methods. Here, we relax this requirement by using stateless actor critic reinforcement learning, which enables non-differentiable rewards. We formulate the instance segmentation problem as graph partitioning and the actor critic predicts the edge weights driven by the rewards, which are based on the conformity of segmented instances to high-level priors on object shape, position or size. The experiments on toy and real datasets demonstrate that we can achieve excellent performance without any direct supervision based only on a rich set of priors

A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation

We propose a novel theoretical framework that generalizes algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes. This framework defines GASP, a Generalized Algorithm for Signed graph Partitioning, and allows us to explore many combinations of different linkage criteria and cannot-link constraints. We prove the equivalence of existing clustering methods to some of those combinations, and introduce new algorithms for combinations which have not been studied. An extensive comparison is performed to evaluate properties of the clustering algorithms in the context of instance segmentation in images, including robustness to noise and efficiency. We show how one of the new algorithms proposed in our framework outperforms all previously known agglomerative methods for signed graphs, both on the competitive CREMI 2016 EM segmentation benchmark and on the CityScapes dataset.Comment: 19 pages, 8 figures, 6 table