Automated Distinct Bone Segmentation from Computed Tomography Images using Deep Learning

Large-scale CT scans are frequently performed for forensic and diagnostic purposes, to plan and direct surgical procedures, and to track the development of bone-related diseases. This often involves radiologists who have to annotate bones manually or in a semi-automatic way, which is a time consuming task. Their annotation workload can be reduced by automated segmentation and detection of individual bones. This automation of distinct bone segmentation not only has the potential to accelerate current workflows but also opens up new possibilities for processing and presenting medical data for planning, navigation, and education. In this thesis, we explored the use of deep learning for automating the segmentation of all individual bones within an upper-body CT scan. To do so, we had to find a network architec- ture that provides a good trade-off between the problem’s high computational demands and the results’ accuracy. After finding a baseline method and having enlarged the dataset, we set out to eliminate the most prevalent types of error. To do so, we introduced an novel method called binary-prediction-enhanced multi-class (BEM) inference, separating the task into two: Distin- guishing bone from non-bone is conducted separately from identifying the individual bones. Both predictions are then merged, which leads to superior results. Another type of error is tack- led by our developed architecture, the Sneaky-Net, which receives additional inputs with larger fields of view but at a smaller resolution. We can thus sneak more extensive areas of the input into the network while keeping the growth of additional pixels in check. Overall, we present a deep-learning-based method that reliably segments most of the over one hundred distinct bones present in upper-body CT scans in an end-to-end trained matter quickly enough to be used in interactive software. Our algorithm has been included in our groups virtual reality medical image visualisation software SpectoVR with the plan to be used as one of the puzzle piece in surgical planning and navigation, as well as in the education of future doctors

Urinary concentrations of GHB and its novel amino acid and carnitine conjugates following controlled GHB administration to humans

Gamma-hydroxybutyrate (GHB) remains a challenging clinical/forensic toxicology drug. Its rapid elimination to endogenous levels mainly causes this. Especially in drug-facilitated sexual assaults, sample collection often occurs later than the detection window for GHB. We aimed to investigate new GHB conjugates with amino acids (AA), fatty acids, and its organic acid metabolites for their suitability as ingestion/application markers in urine following controlled GHB administration to humans. We used LC–MS/MS for validated quantification of human urine samples collected within two randomized, double-blinded, placebo-controlled crossover studies (GHB 50 mg/kg, 79 participants) at approximately 4.5, 8, 11, and 28 h after intake. We found significant differences (placebo vs. GHB) for all but two analytes at 4.5 h. Eleven hours post GHB administration, GHB, GHB-AAs, 3,4-dihydroxybutyric acid, and glycolic acid still showed significantly higher concentrations; at 28 h only GHB-glycine. Three different discrimination strategies were evaluated: (a) GHB-glycine cut-off concentration (1 µg/mL), (b) metabolite ratios of GHB-glycine/GHB (2.5), and (c) elevation threshold between two urine samples (> 5). Sensitivities were 0.1, 0.3, or 0.5, respectively. Only GHB-glycine showed prolonged detection over GHB, mainly when compared to a second time- and subject-matched urine sample (strategy c)

Countless low-surface brightness objects - including spiral galaxies, dwarf galaxies, and noise patterns - have been detected in recent large surveys. Classically, astronomers visually inspect those detections to distinguish between real low-surface brightness galaxies and artefacts. Employing the Dark Energy Survey (DES) and machine learning techniques, Tanoglidis et al. (2020) have shown how this task can be automatically performed by computers. Here, we build upon their pioneering work and further separate the detected low-surface brightness galaxies into spirals, dwarf ellipticals, and dwarf irregular galaxies. For this purpose, we have manually classified 5567 detections from multi-band images from DES and searched for a neural network architecture capable of this task. Employing a hyperparameter search, we find a family of convolutional neural networks achieving similar results as with the manual classification, with an accuracy of 85% for spiral galaxies, 94% for dwarf ellipticals, and 52% for dwarf irregulars. For dwarf irregulars - due to their diversity in morphology - the task is difficult for humans and machines alike. Our simple architecture shows that machine learning can reduce the workload of astronomers studying large data sets by orders of magnitudes, as will be available in the near future with missions such as Euclid.

International audienc

Should Propofol and Alfentanil Be Combined in Patient-Controlled Sedation? A Randomised Controlled Trial Using Pharmacokinetic Simulation

Background: Patient-controlled sedation (PCS) is increasingly used for moderate sedation. Detailed understanding is essential for maintaining safety and giving the most benefit. We wanted to explore the associations between patients’ characteristics, perioperative pain and anxiety, the procedure, and the calculated concentrations at the effect site (Ce) of propofol. We also wanted to analyse the pharmacokinetic profiles of propofol and alfentanil during PCS, and their association with respiratory complications. Methods: 155 patients were double-blinded and randomised to have propofol or propofol and alfentanil for PCS during gynaecological surgery. Pharmacokinetic simulation of Ce and multiple regressions aided the search for correlations between explanatory variables and concentrations of drugs. Results: In group propofol, treatment for incontinence, anterior repair, and the patient’s weight correlated the best (B-coef = 0.20, 0.20 and 0.01; r = 0.69; r² = 0.48). When alfentanil was added, alfentanil and the patient’s weight were associated with Ce of propofol (B-coef = -0.40 and 0.01; r = 0.70; r² = 0.43). Logistic regression indicated that age and Ce of drugs were related to ten cases of respiratory complications. Conclusions: Patients’ weights and the type of surgery performed were associated with the Ce of propofol; this knowledge could be used for refinement of the doses given during PCS. Because the pharmacokinetic profiles of propofol and alfentanil are different, the alfentanil effect becomes predominant during the time course of sedation. In order to reduce the risk of early and late respiratory depression, alfentanil should not be added to propofol in the same syringe.At the time for thesis presentation publication was in status: Manuscript</p

Should Propofol and Alfentanil Be Combined in Patient-Controlled Sedation? A Randomised Controlled Trial Using Pharmacokinetic Simulation

Background: Patient-controlled sedation (PCS) is increasingly used for moderate sedation. Detailed understanding is essential for maintaining safety and giving the most benefit. We wanted to explore the associations between patients’ characteristics, perioperative pain and anxiety, the procedure, and the calculated concentrations at the effect site (Ce) of propofol. We also wanted to analyse the pharmacokinetic profiles of propofol and alfentanil during PCS, and their association with respiratory complications. Methods: 155 patients were double-blinded and randomised to have propofol or propofol and alfentanil for PCS during gynaecological surgery. Pharmacokinetic simulation of Ce and multiple regressions aided the search for correlations between explanatory variables and concentrations of drugs. Results: In group propofol, treatment for incontinence, anterior repair, and the patient’s weight correlated the best (B-coef = 0.20, 0.20 and 0.01; r = 0.69; r² = 0.48). When alfentanil was added, alfentanil and the patient’s weight were associated with Ce of propofol (B-coef = -0.40 and 0.01; r = 0.70; r² = 0.43). Logistic regression indicated that age and Ce of drugs were related to ten cases of respiratory complications. Conclusions: Patients’ weights and the type of surgery performed were associated with the Ce of propofol; this knowledge could be used for refinement of the doses given during PCS. Because the pharmacokinetic profiles of propofol and alfentanil are different, the alfentanil effect becomes predominant during the time course of sedation. In order to reduce the risk of early and late respiratory depression, alfentanil should not be added to propofol in the same syringe.At the time for thesis presentation publication was in status: Manuscript</p