Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

BACKGROUND: Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. OBJECTIVE: Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. METHODS: The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. RESULTS: We demonstrated the model's clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model's generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. CONCLUSIONS: Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways

Recovery of the macroinvertebrate community below a wastewater treatment plant input in a Mediterranean stream

14 Páginas ; 6 Figuras ; 4 Tablas ; 1 ApéndiceWe sampled chlorophyll a, benthic organic matter, and benthic macroinvertebrates in June 2001 in La Tordera stream (Catalonia, NE Spain), receiving a wastewater treatment plant (WWTP) input. Samples were collected in six equidistant transects in three reaches located upstream (UP), few m below (DW1), and 500 m below theWWTP input (DW2). Our first objective was to assess the effects of the point source on the structure and functional organization of the benthic macroinvertebrate community. Our second objective was to determine if the self-purifying capacity of the stream implied differences between the communities of the DW1 and the DW2 reaches. The WWTP input highly increased discharge, nutrient concentrations, and conductivity and decreased dissolved oxygen. At the DW1 and the DW2 reaches, taxa richness, EPT taxa (Ephemeroptera, Plecoptera, and Trichoptera), and Shannon diversity decreased and gatherer relative density increased relative to the UP reach. At the UP reach, CPOM and FPOM standing crops were similar, whereas at the DW1 and the DW2 reaches CPOM was two times higher than FPOM. Detailed analysis showed that major changes in the benthic community occurred abruptly between 80 and 90 m downstream of the point source (middle of the DW1 reach). At this location, chlorophyll a concentration, density of macroinvertebrates, taxa richness, and scraper relative density increased, whereas gatherer relative percentage decreased. The macroinvertebrate community at the DW2 reach was comparable to that at the second middle of the DW1 reach (DW1B). The macroinvertebrate community at the DW1B and the DW2 reaches were quite similar to that at the UP reach, indicating that the recovery capacity of the stream from nutrient enrichment was high.This study was supported by funding of the STREAMES European project (EVK1-CT- 2000-00081).Peer reviewe

Cerium-reduction-induced defects clustering, ordering, and associated microstructure evolution in yttrium-doped ceria

The microstructure, local chemistry, and related evolution of yttrium-doped ceria (YDC) in a reducing environment have been comprehensively characterized by means of electron microscopy and electron energy loss spectroscopy. Long-range ordered features (i.e., superstructures) appear in 25 atom % YDC sintered at high temperature. By combining experimental and atomistic simulation techniques, the ordered structures of defect clusters containing reduced Ce3+ and associated charge-compensating oxygen vacancies have been elucidated. Related microstructure evolution has thereby been explained in terms of the development of ordered defect clusters. In addition, the corresponding diffraction patterns of long-range ordered defect structures embedded in a fluorite lattice have been simulated according to this model, which perfectly matches with experimental diffraction patterns. Such microstructural evolution accompanied by highly ordered defect structures is believed to have strong influence on the ionic conductivity and overall performance of solid oxide fuel cells