Purpose
Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, ...a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms.
Methods
In this work, we describe and present the results of the Head and Neck Auto‐Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands.
Results
This paper presents the quantitative results of this challenge using multiple established error metrics and a well‐defined ranking system. The strengths and weaknesses of the different auto‐segmentation approaches are analyzed and discussed.
Conclusions
The Head and Neck Auto‐Segmentation Challenge 2015 was a good opportunity to assess the current state‐of‐the‐art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure‐specific segmentation algorithms.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Purpose:
Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high ...interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low. In this project, a new approach for automated segmentation of head-neck CT images that combine the robustness of multiatlas-based segmentation with the flexibility of geodesic active contours and the prior knowledge provided by statistical appearance models is presented.
Methods:
The presented approach is using an atlas-based segmentation approach in combination with label fusion in order to initialize a segmentation pipeline that is based on using statistical appearance models and geodesic active contours. An anatomically correct approximation of the segmentation result provided by atlas-based segmentation acts as a starting point for an iterative refinement of this approximation. The final segmentation result is based on using model to image registration and geodesic active contours, which are mutually influencing each other.
Results:
18 CT images in combination with manually segmented labels of parotid glands and brainstem were used in a leave-one-out cross validation scheme in order to evaluate the presented approach. For this purpose, 50 different statistical appearance models have been created and used for segmentation. Dice coefficient (DC), mean absolute distance and max. Hausdorff distance between the autosegmentation results and expert segmentations were calculated. An average Dice coefficient of DC = 0.81 (right parotid gland), DC = 0.84 (left parotid gland), and DC = 0.86 (brainstem) could be achieved.
Conclusions:
The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a segmentation approach based on using multiple atlases in combination with label fusion, the proposed hybrid approach provided more accurate results within a clinically acceptable amount of time.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Our sense of balance and spatial orientation strongly depends on the correct functionality of our vestibular system. Vestibular dysfunction can lead to blurred vision and impaired balance and spatial ...orientation, causing a significant decrease in quality of life. Recent studies have shown that vestibular implants offer a possible treatment for patients with vestibular dysfunction. The close proximity of the vestibular nerve bundles, the facial nerve and the cochlear nerve poses a major challenge to targeted stimulation of the vestibular system. Modeling the electrical stimulation of the vestibular system allows for an efficient analysis of stimulation scenarios previous to time and cost intensive
experiments. Current models are based on animal data or CAD models of human anatomy. In this work, a (semi-)automatic modular workflow is presented for the stepwise transformation of segmented vestibular anatomy data of human vestibular specimens to an electrical model and subsequently analyzed. The steps of this workflow include (i) the transformation of labeled datasets to a tetrahedra mesh, (ii) nerve fiber anisotropy and fiber computation as a basis for neuron models, (iii) inclusion of arbitrary electrode designs, (iv) simulation of quasistationary potential distributions, and (v) analysis of stimulus waveforms on the stimulation outcome. Results obtained by the workflow based on human datasets and the average shape of a statistical model revealed a high qualitative agreement and a quantitatively comparable range compared to data from literature, respectively. Based on our workflow, a detailed analysis of intra- and extra-labyrinthine electrode configurations with various stimulation waveforms and electrode designs can be performed on patient specific anatomy, making this framework a valuable tool for current optimization questions concerning vestibular implants in humans.
The nutritional compositions of 34 edible seaweed products of the
Laminaria sp.,
Undaria pinnatifida,
Hizikia fusiforme and
Porphyra sp. varieties were analyzed.
This study determined amino acid and ...fatty acid (FA) distributions and contents of protein, fat, and total fibre of these seaweed varieties. In general, the marine macroalgae varieties tested demonstrated low lipid contents with 2.3
±
1.6
g/100
g semi-dry sample weight (s.w.) and proved to be a rich source of dietary fibre (46.2
±
8.0
g/100
g
s.w). The pure protein content of seaweed products varied widely (26.6
±
6.3
g/100
g
s.w. in red algae varieties and 12.9
±
6.2
g/100
g
s.w. in brown algae varieties). All essential amino acids were detected in the seaweed species tested and red algae species featured uniquely high concentrations of taurine when compared to brown algae varieties. Interestingly, the FA distribution of seaweed products showed high levels of n-3 FA and demonstrated a nutritionally ideal n-6/n-3 FA ratio. The predominante FA in various seaweed products was eicosapentaenoic acid (C20:5,
n-3) which was at concentrations as high as 50% of total FA content.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Purpose
The segmentation of organs at risk (OAR) is a required precondition for the cancer treatment with image- guided radiation therapy. The automation of the segmentation task is therefore of high ...clinical relevance. Deep learning (DL)-based medical image segmentation is currently the most successful approach, but suffers from the over-presence of the background class and the anatomically given organ size difference, which is most severe in the head and neck (HAN) area.
Methods
To tackle the HAN area-specific class imbalance problem, we first optimize the patch size of the currently best performing general-purpose segmentation framework, the nnU-Net, based on the introduced class imbalance measurement, and second introduce the class adaptive Dice loss to further compensate for the highly imbalanced setting.
Results
Both the patch size and the loss function are parameters with direct influence on the class imbalance, and their optimization leads to a 3% increase in the Dice score and 22% reduction in the 95% Hausdorff distance compared to the baseline, finally reaching
0.8
±
0.15
and
3.17
±
1.7
mm for the segmentation of seven HAN organs using a single and simple neural network.
Conclusion
The patch size optimization and the class adaptive Dice loss are both simply integrable in current DL-based segmentation approaches and allow to increase the performance for class imbalance segmentation tasks.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
We present a new algorithm for peak detection in ballistocardiographic (BCG) signals and use it for heart rate estimation. Systolic complexes of the BCG signal are enhanced and coarse heart beat ...locations estimated. Ejection waves I, J and K are detected simultaneously around coarse locations, only using detection of local maxima and weighted summation of peak heights. Due to a lack of reference BCG annotations, the algorithm's performance is assessed by using the detected peaks for heart rate estimation. On a dataset acquired with a pneumatic BCG system, we evaluate the heart rate estimation performance and compare the introduced algorithm against other methods found in literature. The dataset is gathered from 42 patients in a clinical environment and provides low-quality signals taken from a realistic scenario. With a mean absolute percentage error of 2.58 % at 65 % coverage, the presented method is on par with the best-performing state-of-the-art algorithms investigated. Limits of agreement (5th/95th percentiles) in a comparison with ECG-based heart rate measurements lie within P 5 = -3.63 and P 95 = 5.78 beat/min.
Purpose
Cancer in the head and neck area is commonly treated with radiotherapy. A key step for low-risk treatment is the accurate delineation of organs at risk in the planning imagery. The success of ...deep learning in image segmentation led to automated algorithms achieving human expert performance on certain datasets. However, such algorithms require large datasets for training and fail to segment previously unseen pathologies, where human experts still succeed. As pathologies are rare and large datasets costly to generate, we investigate the effect of: reduced training data, batch sizes and incorporation of prior knowledge.
Methods
The small data problem is studied by training a full-volume segmentation network with the reduced amount of data from the MICCAI 2015 head and neck segmentation challenge. To improve the segmentation, we evaluate the batch size as a hyper-parameter and first study and then incorporate a stacked autoencoder as shape prior into the training process.
Results
We found that using half of the training data (12 images of 25) results in an accuracy drop of only 3% for the segmentation of organs at risk. Also, the batch size turns out to be relevant for the quality of the segmentation when trained with less than half of the data. By applying PCA on the autoencoder’s latent space we achieve a compact and accurate shape model, which is used as a regularizer and significantly improves the segmentation results.
Conclusion
Small training data of up to 12 training images is enough to train accurate head and neck segmentation models. By using a shape prior for regularization, the performance of the segmentation can be improved significantly on the full dataset. When training on fewer than 12 images, the batch size is relevant and models have to be trained much longer until convergence.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Berry seeds are distinguished by longevity though clear scientific appraisals cannot be made. Besides a hard seed coat other protecting substances are presumed in the seeds. Commonly the seeds are ...utilized as a source of oils. After pressing, there is a residue left that is still rich in bioactive ingredients. This paper gives an overview of the health-beneficial ingredients remaining in the residue of various berry seeds (bilberry, cranberry, rose hip, strawberry, elder, and black currant) with special focus on black currant. The fatty acid distribution and the content of fat, tocopherols and tocotrienols, phytosterols, carotenoids, vitamin C, fibre, protein, amino acids, dry matter, ashes, minerals, total phenols (gallic acid equivalent) and antioxidant capacity (TEAC) were determined. The investigation of berry seed press residues revealed that the total phenols and tocopherols were quantitatively the most important features of this material but there were significant differences between batches and cultures.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Objectives
To identify spatial disparities in dental caries experience (measured by dmft (decayed missing filled teeth) index) of children in the city of Braunschweig and to evaluate whether these ...disparities can be explained by sociodemographic characteristics.
Methods
We examined the dental health of children aged 3‐6 years visiting a daycare centre (DCC) in the metropolitan area of Braunschweig between 2009 and 2014 by combining data on dental health from the annual visits of the local health service with aggregated data on sociodemographic factors for Braunschweig's city districts. We assessed longitudinal patterns of change in average dmft index at district level from 2009 to 2014 using a finite mixture model. We analysed spatial autocorrelation of the district's average dmft indices by Moran's I to identify spatial clusters. With a spatial lag model, we evaluated whether sociodemographic risk factors (data from 2012) were associated with high dmft scores (data from 2014) and whether spatial disparities remained after adjusting for these sociodemographic characteristics.
Results
The average dmft index decreased slightly (β=−0.048; P<.03; CI 95% −0.079; −0.017) from 2009 to 2014. The finite mixture model resulted in four different groups of trajectories over time. While three groups showed a decrease in dmft score, one group showed an increase from 2009 to 2014. Moran's I test statistic showed strong evidence for spatial clustering (Moran's I 0.30, P=.002). A cluster of districts with high dmft values was identified in the centre of the city. The spatial lag model showed that both the proportion of unemployed persons (aged 16‐65) and the proportion of persons with migration background were associated with the dmft values at district level. After adjusting for these, no further spatial heterogeneity was observed.
Conclusion
We identified regional clusters for poor dental health in a German city and showed that these clusters can be explained by sociodemographic characteristics. The findings support the need of targeted interventions and prevention measures in regions with less favourable sociodemographic characteristics.
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BFBNIB, CMK, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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