Machine learning of hierarchical clustering to segment 2D and 3D images

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.Comment: 15 pages, 8 figure

Cellular tracking in time-lapse phase contrast images

The quantitative analysis of live cells is a key issue in evaluating biological processes. The current clinical practice involves the application of a tedious and time consuming manual tracking procedure on large amount of data. As a result, automatic tracking systems are currently developed and evaluated. However, problems caused by cellular division, agglomeration, Brownian motion and topology changes are difficult issues that have to be accommodated by automatic tracking techniques. In this paper, we detail the development of a fully automated multi-target tracking system that is able to deal with Brownian motion and cellular division. During the tracking process our approach includes the neighbourhood relationship and motion history to enforce the cellular tracking continuity in the spatial and temporal domain. The experimental results reported in this paper indicate that our method is able to accurately track cellular structures in time-lapse data

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far. Convolutional nets were first applied to neuronal boundary detection a dozen years ago, and have now achieved impressive accuracy on clean images. Robust handling of image defects is a major outstanding challenge. Convolutional nets are also being employed for other tasks in neural circuit reconstruction: finding synapses and identifying synaptic partners, extending or pruning neuronal reconstructions, and aligning serial section images to create a 3D image stack. Computational systems are being engineered to handle petavoxel images of cubic millimeter brain volumes

Learned versus Hand-Designed Feature Representations for 3d Agglomeration

For image recognition and labeling tasks, recent results suggest that machine learning methods that rely on manually specified feature representations may be outperformed by methods that automatically derive feature representations based on the data. Yet for problems that involve analysis of 3d objects, such as mesh segmentation, shape retrieval, or neuron fragment agglomeration, there remains a strong reliance on hand-designed feature descriptors. In this paper, we evaluate a large set of hand-designed 3d feature descriptors alongside features learned from the raw data using both end-to-end and unsupervised learning techniques, in the context of agglomeration of 3d neuron fragments. By combining unsupervised learning techniques with a novel dynamic pooling scheme, we show how pure learning-based methods are for the first time competitive with hand-designed 3d shape descriptors. We investigate data augmentation strategies for dramatically increasing the size of the training set, and show how combining both learned and hand-designed features leads to the highest accuracy

Analýza znečisťujúcich látok vo vybraných monitorovacích staniciach mesta Košice za obdobie 2008-2010

In general, air quality is determined from the concentrations of pollutants in ambient air. Air quality criteria (limit and target values, margin of tolerances, upper and lower assessment thresholds) are based on the current legislative framework. In Slovakia, the air quality criteria are imposed by Decree No 360/2010 Coll, on air quality, of the Ministry of Environment [1]. In relation to the implemented measurements, it is necessary to choose effective tools needed for the pre-processing and post-processing of overall air quality assessment. The article aims at applying suitable GIS tools in the assessment process of air quality in the Košice agglomeration for the selected period 2008-2010. In the overall assessment of the area in question, the processing of information on emissions declared as particulate matter (PM) was considered in terms of a conservative approach to the assessment of air quality for PM10. For the assessment, the yearbooks and reports on air quality in Slovakia in 2008-2010 of the Slovak Hydrometeorological Institute (SHMI) and professional publications were used.Vo všeobecnosti kvalita ovzdušia je stanovená z obsahu znečisťujúcich látok vo vonkajšom ovzduší. Kritériá kvality ovzdušia (limitné a cieľové hodnoty, medze tolerancie, horné a dolné medze na hodnotenie) vychádzajú z platného legislatívneho rámca. V podmienkach SR kritéria kvality ovzdušia vyplývajú z vyhlášky MŽP SR č. 360/2010 Z.z. o kvalite ovzdušia [1]. V nadväznosti na realizované merania je potrebné zvoliť efektívne nástroje potrebné pre preprocesing a postprocesing procesu plošného hodnotenia kvality ovzdušia. Príspevok si kladie za cieľ aplikovať vhodné nástroje GIS do hodnotiaceho procesu kvality ovzdušia v aglomerácií Košice za vybrané obdobie rokov 2008 - 2010. Pri celkovom zhodnotení predmetnej oblasti bolo uvažované so spracovaním informácií o emisiách deklarovaných ako tuhé znečisťujúce látky (TZL) v zmysle konzervatívneho prístupu k hodnoteniu kvality ovzdušia za PM10. Pre posudzovanie boli použité ročenky a správy pre kvalitu ovzdušia v SR za rok 2008 - 2010 z SHMÚ a odborné publikácie