Een archeologische evaluatie en waardering van houtskoolmeilers in het Zoerselbos (Zoersel, provincie Antwerpen)

Dit waarderend onderzoek van de restanten van houtskoolmeilers in het Zoerselbos biedt een historische en archeologische onderbouwing voor het behoud van dit bijzondere erfgoed. Naast het Grotenhout (Gierle) en het s Herenbos (Malle) is het Zoerselbos één van de weinige oudboskernen in het Kempisch district. Het behoort tot de meest waardevolle en belangrijke bosgebieden in Vlaanderen. Het gebied is beschermd als landschap en maakt deel uit van een ankerplaats. Bovenop vroeger onderzoek naar o.a. de ecohydrologie en biotoopkarteringen, die naast bijzondere ecologische elementen ook al cultuurhistorische elementen opnamen zoals houtskoolmeilers, ijskelders, mottes, laagovens, biedt dit interdisciplinaire onderzoeksproject op basis van archiefmateriaal, natuurhistorische waarden en landschappelijke relicten een beter beeld van dit oude bos als historisch (gebruikt) landschap. Concreet levert het archeologisch en paleo-ecologisch terreinonderzoek inzicht in het aantal, de ligging, het type, de opbouw en de bijhorende structuren, de omvang, de gebruikte houtsoorten, de ouderdom, de diachronische ontwikkeling, de seizoenaliteit en het hergebruik van de historische houtskoolmeilers in Zoerselbos. Restanten van houtskoolmeilers vormen historisch erfgoed dat buiten de oude bosgebieden door grondbewerking volledig verdwenen is. Het onderzoek in Zoerselbos toonde ook aan hoe weinig er van deze houtskoolmeilers gekend is. Het rapport besluit met een concreet beschermingsvoorstel en met aanbevelingen voor verder onderzoek en beheer van deze relicten

Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET

Background: Positron emission tomography (PET) is routinely used for cancer staging and treatment follow-up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV—including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge. Methods: In this study, we compare two semi-automatic artificial intelligence (AI)-based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a convolutional neural network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the Jaccard coefficient (JC). Additionally, the approaches are externally tested on a fully independent test–retest dataset. The repeatability of the methods is compared with those of two majority vote (MV2, MV3) approaches, 41%SUVMAX, and a SUV > 4 segmentation (SUV4). Repeatability is assessed with test–retest coefficients (TRT%) and intraclass correlation coefficient (ICC). An ICC > 0.9 was regarded as representing excellent repeatability. Results: The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73). Both segmentation approaches outperformed most other conventional segmentation methods in terms of test–retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUVMAX: 28.1%, SUV4: 18.1%) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUVMAX: 0.68). Conclusion: The semi-automatic AI-based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation

Fingerprints for Structural Defects in Poly(thienylene vinylene) (PTV): A Joint Theoretical–Experimental NMR Study on Model Molecules

In the field of plastic electronics, low band gap conjugated polymers like poly(thienylene vinylene) (PTV) and its derivatives are a promising class of materials that can be obtained with high molecular weight via the so-called dithiocarbamate precursor route. We have performed a joint experimental- theoretical study of the full NMR chemical shift assignment in a series of thiophene-based model compounds, which aims at (i) benchmarking the quantum-chemical calculations against experiments, (ii) identifying the signature of possible structural defects that can appear during the polymerization of PTV's, namely head-to-head and tail-to-tail defects, and (iii) defining a criterion regarding regioregularity

Plausibility and redundancy analysis to select FDG-PET textural features in non-small cell lung cancer

Contains fulltext : 232784.pdf (Publisher’s version ) (Open Access)BACKGROUND: Radiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non-small cell lung cancer (NSCLC) dataset. MATERIALS AND METHODS: The proposed selection procedure consists of several steps. A priori, we only include features that were found to be reproducible in a multicenter setting. Next, we apply a voxel randomization step to identify features that reflect actual textural information, that is, that yield in 90% of the patient scans a value significantly different from random texture. Finally, the remaining features were correlated with standard PET metrics to further remove redundancy with common PET metrics. The selection procedure was performed for different volume ranges, that is, excluding lesions with smaller volumes in order to assess the effect of tumor size on the results. To exemplify our procedure, the selected features were used to predict 1-yr survival in a dataset of 150 NSCLC patients. A predictive model was built using volume as predictive factor for smaller, and one of the selected features as predictive factor for bigger lesions. The prediction accuracy of the both models were compared with the prediction accuracy of volume. RESULTS: The number of selected features depended on the lesion size included in the analysis. When including the whole dataset, from 19 features reflecting actual texture only two were found to be not strongly correlated with conventional PET metrics. When excluding lesions smaller than 11.49 and 33.10 mL (25 and 50 percentile of the dataset), four out of 27 features and 13 out of 29 features remained after eliminating features highly correlated with standard PET metrics. When excluding lesions smaller than 103.9 mL (75 percentile), 33 out of 53 features remained. For larger lesions, some of these features outperformed volume in terms of classification accuracy (increase of 4-10%). The combination of using volume as predictor for smaller and one of the selected features for larger lesions also improved the accuracy when compared with volume only (increase from 72% to 76%). CONCLUSION: When performing radiomic analysis for smaller lesions, it should be first carefully investigated if a textural feature reflects actual heterogeneity information. Next, verification of the absence of correlation with all conventional PET metrics is essential in order to assess the additional value of radiomic features. Radiomic analysis with lesions larger than 11.4 mL might give additional information to conventional metrics while at the same time reflecting actual tumor texture. Using a combination of volume and one of the selected features for prediction yields promise to increase accuracy and reliability of a radiomic model

Implantaten en hun succes na bestraling

edition: 1ststatus: publishe