PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion.

BACKGROUND AND OBJECTIVE Despite fast evolution cycles in deep learning methodologies for medical imaging in radiotherapy, auto-segmentation solutions rarely run in clinics due to the lack of open-source frameworks feasible for processing DICOM RT Structure Sets. Besides this shortage, available open-source DICOM RT Structure Set converters rely exclusively on 2D reconstruction approaches leading to pixelated contours with potentially low acceptance by healthcare professionals. PyRaDiSe, an open-source, deep learning framework independent Python package, addresses these issues by providing a framework for building auto-segmentation solutions feasible to operate directly on DICOM data. In addition, PyRaDiSe provides profound DICOM RT Structure Set conversion and processing capabilities; thus, it applies also to auto-segmentation-related tasks, such as dataset construction for deep learning model training. METHODS The PyRaDiSe package follows a holistic approach and provides DICOM data handling, deep learning model inference, pre-processing, and post-processing functionalities. The DICOM data handling allows for highly automated and flexible handling of DICOM image series, DICOM RT Structure Sets, and DICOM registrations, including 2D-based and 3D-based conversion from and to DICOM RT Structure Sets. For deep learning model inference, extending given skeleton classes is straightforwardly achieved, allowing for employing any deep learning framework. Furthermore, a profound set of pre-processing and post-processing routines is included that incorporate partial invertibility for restoring spatial properties, such as image origin or orientation. RESULTS The PyRaDiSe package, characterized by its flexibility and automated routines, allows for fast deployment and prototyping, reducing efforts for auto-segmentation pipeline implementation. Furthermore, while deep learning model inference is independent of the deep learning framework, it can easily be integrated into famous deep learning frameworks such as PyTorch or Tensorflow. The developed package has successfully demonstrated its capabilities in a research project at our institution for organs-at-risk segmentation in brain tumor patients. Furthermore, PyRaDiSe has shown its conversion performance for dataset construction. CONCLUSIONS The PyRaDiSe package closes the gap between data science and clinical radiotherapy by enabling deep learning segmentation models to be easily transferred into clinical research practice. PyRaDiSe is available on https://github.com/ubern-mia/pyradise and can be installed directly from the Python Package Index using pip install pyradise

The predictive value of segmentation metrics on dosimetry in organs at risk of the brain.

BACKGROUND Fully automatic medical image segmentation has been a long pursuit in radiotherapy (RT). Recent developments involving deep learning show promising results yielding consistent and time efficient contours. In order to train and validate these systems, several geometric based metrics, such as Dice Similarity Coefficient (DSC), Hausdorff, and other related metrics are currently the standard in automated medical image segmentation challenges. However, the relevance of these metrics in RT is questionable. The quality of automated segmentation results needs to reflect clinical relevant treatment outcomes, such as dosimetry and related tumor control and toxicity. In this study, we present results investigating the correlation between popular geometric segmentation metrics and dose parameters for Organs-At-Risk (OAR) in brain tumor patients, and investigate properties that might be predictive for dose changes in brain radiotherapy. METHODS A retrospective database of glioblastoma multiforme patients was stratified for planning difficulty, from which 12 cases were selected and reference sets of OARs and radiation targets were defined. In order to assess the relation between segmentation quality -as measured by standard segmentation assessment metrics- and quality of RT plans, clinically realistic, yet alternative contours for each OAR of the selected cases were obtained through three methods: (i) Manual contours by two additional human raters. (ii) Realistic manual manipulations of reference contours. (iii) Through deep learning based segmentation results. On the reference structure set a reference plan was generated that was re-optimized for each corresponding alternative contour set. The correlation between segmentation metrics, and dosimetric changes was obtained and analyzed for each OAR, by means of the mean dose and maximum dose to 1% of the volume (Dmax 1%). Furthermore, we conducted specific experiments to investigate the dosimetric effect of alternative OAR contours with respect to the proximity to the target, size, particular shape and relative location to the target. RESULTS We found a low correlation between the DSC, reflecting the alternative OAR contours, and dosimetric changes. The Pearson correlation coefficient between the mean OAR dose effect and the Dice was -0.11. For Dmax 1%, we found a correlation of -0.13. Similar low correlations were found for 22 other segmentation metrics. The organ based analysis showed that there is a better correlation for the larger OARs (i.e. brainstem and eyes) as for the smaller OARs (i.e. optic nerves and chiasm). Furthermore, we found that proximity to the target does not make contour variations more susceptible to the dose effect. However, the direction of the contour variation with respect to the relative location of the target seems to have a strong correlation with the dose effect. CONCLUSIONS This study shows a low correlation between segmentation metrics and dosimetric changes for OARs in brain tumor patients. Results suggest that the current metrics for image segmentation in RT, as well as deep learning systems employing such metrics, need to be revisited towards clinically oriented metrics that better reflect how segmentation quality affects dose distribution and related tumor control and toxicity

Impact of random outliers in auto-segmented targets on radiotherapy treatment plans for glioblastoma.

AIMS To save time and have more consistent contours, fully automatic segmentation of targets and organs at risk (OAR) is a valuable asset in radiotherapy. Though current deep learning (DL) based models are on par with manual contouring, they are not perfect and typical errors, as false positives, occur frequently and unpredictably. While it is possible to solve this for OARs, it is far from straightforward for target structures. In order to tackle this problem, in this study, we analyzed the occurrence and the possible dose effects of automated delineation outliers. METHODS First, a set of controlled experiments on synthetically generated outliers on the CT of a glioblastoma (GBM) patient was performed. We analyzed the dosimetric impact on outliers with different location, shape, absolute size and relative size to the main target, resulting in 61 simulated scenarios. Second, multiple segmentation models where trained on a U-Net network based on 80 training sets consisting of GBM cases with annotated gross tumor volume (GTV) and edema structures. On 20 test cases, 5 different trained models and a majority voting method were used to predict the GTV and edema. The amount of outliers on the predictions were determined, as well as their size and distance from the actual target. RESULTS We found that plans containing outliers result in an increased dose to healthy brain tissue. The extent of the dose effect is dependent on the relative size, location and the distance to the main targets and involved OARs. Generally, the larger the absolute outlier volume and the distance to the target the higher the potential dose effect. For 120 predicted GTV and edema structures, we found 1887 outliers. After construction of the planning treatment volume (PTV), 137 outliers remained with a mean distance to the target of 38.5 ± 5.0 mm and a mean size of 1010.8 ± 95.6 mm3. We also found that majority voting of DL results is capable to reduce outliers. CONCLUSIONS This study shows that there is a severe risk of false positive outliers in current DL predictions of target structures. Additionally, these errors will have an evident detrimental impact on the dose and therefore could affect treatment outcome

Dose Guidance for Radiotherapy-Oriented Deep Learning Segmentation

Deep learning-based image segmentation for radiotherapy is intended to speed up the planning process and yield consistent results. However, most of these segmentation methods solely rely on distribution and geometry-associated training objectives without considering tumor control and the sparing of healthy tissues. To incorporate dosimetric effects into segmentation models, we propose a new training loss function that extends current state-of-the-art segmentation model training via a dose-based guidance method. We hypothesized that adding such a dose-guidance mechanism improves the robustness of the segmentation with respect to the dose (i.e., resolves distant outliers and focuses on locations of high dose/dose gradient). We demonstrate the effectiveness of the proposed method on Gross Tumor Volume segmentation for glioblastoma treatment. The obtained dosimetry-based results show reduced dose errors relative to the ground truth dose map using the proposed dosimetry-segmentation guidance, outperforming state-of-the-art distribution and geometry-based segmentation losses

The old, unique C1 chondrite Flensburg – insight into the first processes of aqueous alteration, brecciation, and the diversity of water-bearing parent bodies and lithologies

International audienceOn September 12, 2019 at 12:49:48 (UT) a bolide was observed by hundreds of eye-witnesses from the Netherlands, Germany, Belgium, Denmark and the UK. One day later a small meteorite stone was found by accident in Flensburg. The presence of short-lived cosmogenic radionuclides with half-lives as short as 16 days proves the recent exposure of the found object to cosmic rays in space linking it clearly to the bolide event. An exceptionally short exposure time of ∼5000 years was determined. The 24.5 g stone has a fresh black fusion crust, a low density of <2 g/cm3, and a magnetic susceptibility of logχ = 4.35 (χ in 10−9 m3/kg). The rock consists of relict chondrules and clusters of sulfide and magnetite grains set in a fine-grained matrix. The most abundant phases are phyllosilicates. Carbonates (∼3.9 vol.%) occur as calcites, dolomites, and a Na-rich phase. The relict chondrules (often surrounded by sulfide laths) are free of anhydrous silicates and contain abundant serpentine. Lithic clasts are also surrounded by similar sulfide laths partly intergrown with carbonates. 53Mn-53Cr ages of carbonates in Flensburg indicate that brecciation and contemporaneous formation of the pyrrhotite-carbonate intergrowths by hydrothermal activities occurred no later than 4564.6 ± 1.0 Ma (using the angrite D'Orbigny as the Mn-Cr age anchor). This corresponds to 2.6 ± 1.0 or 3.4 ± 1.0 Ma after formation of CAIs, depending on the exact absolute age of CAIs. This is the oldest dated evidence for brecciation and carbonate formation, which likely occurred during parent body growth and incipient heating due to decay of 26Al.In the three oxygen isotope diagram, Flensburg plots at the 16O-rich end of the CM chondrite field and in the transition field to CV-CK-CR chondrites. The mass-dependent Te isotopic composition of Flensburg is slightly different from mean CM chondrites and is most similar to those of the ungrouped C2 chondrite Tagish Lake. On the other hand, 50Ti and 54Cr isotope anomalies indicate that Flensburg is similar to CM chondrites, as do the ∼10 wt.% H2O of the bulk material. Yet, the bulk Zn, Cu, and Pb concentrations are about 30% lower than those of mean CM chondrites. The He, Ne, and Ar isotopes of Flensburg show no solar wind contribution; its trapped noble gas signature is similar to that of CMs with a slightly lower concentration of 20Netr.Based on the bulk H, C, and N elemental abundances and isotopic compositions, Flensburg is unique among chondrites, because it has the lightest bulk H and N isotopic compositions of any type 1 or 2 chondrite investigated so far. Moreover, the number of soluble organic compounds in Flensburg is even lower than that of the brecciated CI chondrite Orgueil.The extraordinary significance of Flensburg is evident from the observation that it represents the oldest chondrite sample in which the contemporaneous episodes of aqueous alteration and brecciation have been preserved. The characterization of a large variety of carbonaceous chondrites with different alteration histories is important for interpreting returned samples from the OSIRIS-REx and Hayabusa 2 missions