Automated Detection of Canola/Rapeseed Cultivation from Space: Application of new Algorithms for the Identi cation of Agricultural Plants with Multispectral Satellite Data on the Example of Canola Cultivation

The dispersal of new genes resulting from the cultivation of genetically modified plants holds risks that are difficult to assess. In this context the situation of cultivation is of particular interest since fields are potential sources of the transfer of new genes to non-modified or related plants. The aim of this work is the identification of canola cultivation areas in northern Germany in the studied period from 1995 to 2002. The sizes of the fields and the investigation area pose requirements on the satellite data best met the LANDSAT Thematic Mapper and Enhanced Thematic Mapper and the Indian Remote Sensing Satellite Linear Imaging Scanning Spectrometer/3.The first processing step, the georectification is done by a passpoint correlation which is improved by an additional correction step, based on the correlation of image clips.The next processing step is the identification of clouds and their shadows. Opaque clouds can be identified by their brightness and low top temperature. Thin clouds are identified based on the Haze Optimized Transform method. The third processing step, the classification, is performed by the Mahalanobis Distance Clasifier (MDC) because it only requires training data for one single surface type. The accuracy of the MDC is enhanced by a segmentation of the MDC result used to identify single wrongly identified pixels and to perform region growing to include pixels missed by the MDC.The results are approximated by rectangles of equal orientation and area which allows a simple evaluation of the field distances and other parameters of interest. The results are used to produce statistics to investigate these parameters for the cultivation of canola in northern Germany. The results of the classification are compared to validation data, i.e., edges and positions of known canola fields and agricultural statistics for 1995 and 1999. This validation showed that the total acreage of canola is identified with 70 to 90% accuracy

A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion by dynamic contrast-agent imaging: Direct simulation and inverse tracer-kinetic procedures

Dynamic contrast-enhanced (DCE) perfusion imaging has shown great potential to non-invasively assess cancer development and its treatment by their characteristic tissue signatures. Different tracer kinetics models are being applied to estimate tissue and tumor perfusion parameters from DCE perfusion imaging. The goal of this work is to provide an in silico model-based pipeline to evaluate how these DCE imaging parameters may relate to the true tissue parameters. As histology data provides detailed microstructural but not functional parameters, this work can also help to better interpret such data. To this aim in silico vasculatures are constructed and the spread of contrast agent in the tissue is simulated. As a proof of principle we show the evaluation procedure of two tracer kinetic models from in silico contrast-agent perfusion data after a bolus injection. Representative microvascular arterial and venous trees are constructed in silico. Blood flow is computed in the different vessels. Contrast-agent input in the feeding artery, intra-vascular transport, intra-extravascular exchange and diffusion within the interstitial space are modeled. From this spatiotemporal model, intensity maps are computed leading to in silico dynamic perfusion images. Various tumor vascularizations (architecture and function) are studied and show spatiotemporal contrast imaging dynamics characteristic of in vivo tumor morphotypes. The Brix II also called 2CXM, and extended Tofts tracer-kinetics models common in DCE imaging are then applied to recover perfusion parameters that are compared with the ground truth parameters of the in silico spatiotemporal models. The results show that tumor features can be well identified for a certain permeability range. The simulation results in this work indicate that taking into account space explicitly to estimate perfusion parameters may lead to significant improvements in the perfusion interpretation of the current tracer-kinetics models

hendriklaue/qiba-pdf-evaluation-tool: Moved QDET to Github

<p>We moved :-). Note that this release is untested.</p><p><strong>Full Changelog</strong>: https://github.com/hendriklaue/qiba-pdf-evaluation-tool/commits/moved-to-github</p&gt

Automatische Detektion des Rapsanbaus aus dem Weltraum:Anwendung von neuen Algorithmen zur automatischen Identi kation von landwirtschaftlichen Anbau 0chen mit multispektralen Satellitendaten am Beispiel von Raps

The dispersal of new genes resulting from the cultivation of genetically modified plants holds risks that are difficult to assess. In this context the situation of cultivation is of particular interest since fields are potential sources of the transfer of new genes to non-modified or related plants. The aim of this work is the identification of canola cultivation areas in northern Germany in the studied period from 1995 to 2002. The sizes of the fields and the investigation area pose requirements on the satellite data best met the LANDSAT Thematic Mapper and Enhanced Thematic Mapper and the Indian Remote Sensing Satellite Linear Imaging Scanning Spectrometer/3.The first processing step, the georectification is done by a passpoint correlation which is improved by an additional correction step, based on the correlation of image clips.The next processing step is the identification of clouds and their shadows. Opaque clouds can be identified by their brightness and low top temperature. Thin clouds are identified based on the Haze Optimized Transform method. The third processing step, the classification, is performed by the Mahalanobis Distance Clasifier (MDC) because it only requires training data for one single surface type. The accuracy of the MDC is enhanced by a segmentation of the MDC result used to identify single wrongly identified pixels and to perform region growing to include pixels missed by the MDC.The results are approximated by rectangles of equal orientation and area which allows a simple evaluation of the field distances and other parameters of interest. The results are used to produce statistics to investigate these parameters for the cultivation of canola in northern Germany. The results of the classification are compared to validation data, i.e., edges and positions of known canola fields and agricultural statistics for 1995 and 1999. This validation showed that the total acreage of canola is identified with 70 to 90% accuracy

Automated Detection of Portal Fields and Central Veins in Whole-Slide Images of Liver Tissue

Many physiological processes and pathological phenomena in the liver tissue are spatially heterogeneous. At a local scale, biomarkers can be quantified along the axis of the blood flow, from portal fields (PFs) to central veins (CVs), i.e., in zonated form. This requires detecting PFs and CVs. However, manually annotating these structures in multiple whole-slide images is a tedious task. We describe and evaluate a fully automated method, based on a convolutional neural network, for simultaneously detecting PFs and CVs in a single stained section. Trained on scans of hematoxylin and eosin-stained liver tissue, the detector performed well with an F1 score of 0.81 compared to annotation by a human expert. It does, however, not generalize well to previously unseen scans of steatotic liver tissue with an F1 score of 0.59. Automated PF and CV detection eliminates the bottleneck of manual annotation for subsequent automated analyses, as illustrated by two proof-of-concept applications: We computed lobulus sizes based on the detected PF and CV positions, where results agreed with published lobulus sizes. Moreover, we demonstrate the feasibility of zonated quantification of biomarkers detected in different stainings based on lobuli and zones obtained from the detected PF and CV positions. A negative control (hematoxylin and eosin) showed the expected homogeneity, a positive control (glutamine synthetase) was quantified to be strictly pericentral, and a plausible zonation for a heterogeneous F4/80 staining was obtained. Automated detection of PFs and CVs is one building block for automatically quantifying physiologically relevant heterogeneity of liver tissue biomarkers. Perspectively, a more robust and automated assessment of zonation from whole-slide images will be valuable for parameterizing spatially resolved models of liver metabolism and to provide diagnostic information

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCEâ MRI derived biomarkers in multicenter oncology trials

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149499/1/jmri26805.pd