Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data

Purpose: To investigate the use of four-dimensional (4D) co-occurrence-based texture analysis to distinguish between nonmalignant and malignant tissues in dynamic contrast-enhanced (DCE) MR images. Materials and Methods: 4D texture analysis was performedon DCE-MRI data sets of breast lesions. A model-free neural network-based classification system assigned each voxel a "nonmalignant" or "malignant" label based on the textural features. The classification results were compared via receiver operating characteristic (ROC) curve analysis with the manual lesion segmentation produced by two radiologists (observers 1 and 2). Results: The mean sensitivity and specificity of the classifier agreed with the mean observer 2 performance when compared with segmentations by observer 1 for a 95% confidence interval, using a two-sided t-test with α = 0.05. The results show that an area under the ROC curve (Az) of 0.99948, 0.99867, and 0.99957 can be achieved by comparing the classifier vs. observer 1, classifier vs. union of both observers, and classifier vs. intersection of both observers, respectively. Conclusion: This study shows that a neural network classifier based on 4D texture analysis inputs can achieve a performance comparable to that achieved by human observers, and that further research in this area is warranted. © 2007 Wiley-Liss, Inc

The impact of neogene grassland expansion and aridification on the isotopic composition of continental precipitation

The late Cenozoic was a time of global cooling, increased aridity, and expansion of grasslands. In the last two decades numerous records of oxygen isotopes have been collected to assess plant ecological changes, understand terrestrial paleoclimate, and to determine the surface history of mountain belts. The δ¹⁸(O) values of these records, in general, increase from the mid-Miocene to the Recent. We suggest that these records record an increase in aridity and expansion of grasslands in midlatitude continental regions. We use a nondimensional isotopic vapor transport model coupled with a soil water isotope model to evaluate the role of vapor recycling and transpiration by different plant functional types. This analysis shows that increased vapor recycling associated with grassland expansion along with biomechanistic changes in transpiration by grasses themselves conspires to lower the horizontal gradient in the δ¹⁸(O) of atmospheric vapor as an air mass moves into continental interiors. The resulting signal at a given inland site is an increase in δ¹⁸(O) of precipitation with the expansion of grasslands and increasing aridity, matching the general observed trend in terrestrial Cenozoic δ¹⁸(O) records. There are limits to the isotopic effect that are induced by vapor recycling, which we refer to here as a “hydrostat.” In the modern climate, this hydrostatic limit occurs at approximately the boundary between forest and grassland ecosystems

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Lineage-specific evolution of the vertebrate Otopetrin gene family revealed by comparative genomic analyses

Background: Mutations in the Otopetrin 1 gene (Otop1) in mice and fish produce an unusual bilateral vestibular pathology that involves the absence of otoconia without hearing impairment. The encoded protein, Otop1, is the only functionally characterized member of the Otopetrin Domain Protein (ODP) family; the extended sequence and structural preservation of ODP proteins in metazoans suggest a conserved functional role. Here, we use the tools of sequence-and cytogenetic-based comparative genomics to study the Otop1 and the Otop2-Otop3 genes and to establish their genomic context in 25 vertebrates. We extend our evolutionary study to include the gene mutated in Usher syndrome (USH) subtype 1G (Ush1g), both because of the head-to-tail clustering of Ush1g with Otop2 and because Otop1 and Ush1g mutations result in inner ear phenotypes. Results: We established that OTOP1 is the boundary gene of an inversion polymorphism on human chromosome 4p16 that originated in the common human-chimpanzee lineage more than 6 million years ago. Other lineage-specific evolutionary events included a three-fold expansion of the Otop genes in Xenopus tropicalis and of Ush1g in teleostei fish. The tight physical linkage between Otop2 and Ush1g is conserved in all vertebrates. To further understand the functional organization of the Ushg1-Otop2 locus, we deduced a putative map of binding sites for CCCTC-binding factor (CTCF), a mammalian insulator transcription factor, from genome-wide chromatin immunoprecipitation-sequencing (ChIP-seq) data in mouse and human embryonic stem (ES) cells combined with detection of CTCF-binding motifs. Conclusions: The results presented here clarify the evolutionary history of the vertebrate Otop and Ush1g families, and establish a framework for studying the possible interaction(s) of Ush1g and Otop in developmental pathways

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment. Tumor-infiltrating lymphocytes (TILs) were identified from standard pathology cancer images by a deep-learning-derived \u201ccomputational stain\u201d developed by Saltz et al. They processed 5,202 digital images from 13 cancer types. Resulting TIL maps were correlated with TCGA molecular data, relating TIL content to survival, tumor subtypes, and immune profiles

Adaptive Decomposition and Remapping Algorithms for Object-Space-Parallel Direct Volume Rendering of Unstructured Grids

Cataloged from PDF version of article.Object space (OS) parallelization of an efficient direct volume rendering algorithm for unstructured grids on distributed-memory architectures is investigated. The adaptive OS decomposition problem is modeled as a graph partitioning (GP) problem using an efficient and highly accurate estimation scheme for view-dependent node and edge weighting. In the proposed model, minimizing the cutsize corresponds to minimizing the parallelization overhead due to the data communication and redundant computation/storage while maintaining the GP balance constraint corresponds to maintaining the computational load balance in parallel rendering. A GP-based, view-independent cell clustering scheme is introduced to induce more tractable view-dependent computational graphs for successive visualizations. As another contribution, a graph-theoretical remapping model is proposed as a solution to the general remapping problem and is used in minimization of the cell-data migration overhead. The remapping tool RM-MeTiS is developed by modifying the GP tool MeTiS and is used in partitioning the remapping graphs. Experiments are conducted using benchmark datasets on a 28-node PC cluster to evaluate the performance of the proposed models. © 2006 Elsevier Inc. All rights reserved