Interactive dance choreography assistance

Creative support for the performing arts is prevalent in many fields, however, for the art of dance, automated tools supporting creativity have been scarce. In this research, we describe ongoing research into (semi)automatic automated creative choreography support. Based on state-of-the-art and a survey among 54 choreographers we establish functionalities and requirements for a choreography assistance tool, including the semantic levels at which it should operate and communicate with the end-users. We describe a user study with a prototype tool which presents choreography alternatives using various simple strategies in three dance styles. The results show that the needs for such a tool vary based on the dance discipline. In a second user study, we investigate various methods of presenting choreography variations. Here, we evaluate four presentation methods: textual descriptions, 2D animations, 3D animations and auditory instructions in two different dance styles. The outcome of the expert survey shows that the tool is effective in communicating the variations to the experts and that they express a preference for 3D animations. Based on these results, we propose a design for an interactive dance choreography assistant tool

Automatic classification of focal liver lesions based on clinical DCE-MR and T2-weighted images:a feasibility study

Focal liver lesion classification is an important part of diagnostics. In clinical practice, T2-weighted (T2W) and dynamic contrast enhanced (DCE) MR images are used to determine the type of lesion. For automatic liver lesion classification only T2W images are exploited. In this feasibility study, a multi-modal approach for automatic lesion classification of five lesion classes (adenoma, cyst, haemangioma, HCC, and metastasis) is studied. Features are derived from four sets: (A) non-corrected, and (B) motion corrected DCE-MRI, (C) T2W images, and (D) B+C combined, originating from 43 patients. An extremely randomized forest is used as classifier. The results show that motion corrected DCE-MRI features are a valuable addition to the T2W features, and improve the accuracy in discriminating benign and malignant lesions, as well as the classification of the five lesion classes. The multimodal approach shows promising results for an automatic liver lesion classification

Motion correction of dynamic contrast enhanced MRI of the liver

Motion correction of dynamic contrast enhanced magnetic resonance images (DCE-MRI) is a challenging task, due to changes in image appearance. In this study a groupwise registration, using a principle component analysis (PCA) based metric, is evaluated for clinical DCE MRI of the liver. The groupwise registration transforms the images to a common space, rather than to a reference volume as conventional pairwise methods do, and computes the similarity metric on all volumes simultaneously. This groupwise registration method is compared to a pairwise approach using a mutual information metric. Clinical DCE MRI of the abdomen of eight patients were included. Per patient one lesion in the liver was manually segmented in all temporal images (N=16). The registered images were compared for accuracy, spatial and temporal smoothness after transformation, and lesion volume change. Compared to a pairwise method or no registration, groupwise registration provided better alignment. In our recently started clinical study groupwise registered clinical DCE MRI of the abdomen of nine patients were scored by three radiologists. Groupwise registration increased the assessed quality of alignment. The gain in reading time for the radiologist was estimated to vary from no difference to almost a minute. A slight increase in reader confidence was also observed. Registration had no added value for images with little motion. In conclusion, the groupwise registration of DCE MR images results in better alignment than achieved by pairwise registration, which is beneficial for clinical assessment

SLIMMER diabetes voorkomen in de eerste lijn

De prevalentie van diabetes is de afgelopen jaren flink gestegen. Onderzoek toont aan dat leefstijlverandering diabetes mellitus type 2 bij hoogrisicopatiënten kan uitstellen of voorkomen. De implementatie en effectiviteit van preventieprogramma’s in de praktijk blijft echter een uitdaging vanwege de noodzakelijke aanpassing aan de lokale context en beperkte (financiële) middelen. Omdat in Nederland nog geen effectief diabetespreventieprogramma voor de eerste lijn bestond, is het SLIMMER-programma ontwikkeld. SLIMMER is een gecombineerde leefstijlinterventie waarbij mensen gedurende tien maanden begeleid worden om gezonder te gaan eten en meer te bewegen. In deze beschouwing bespreken we de effectiviteit van het SLIMMER-programma in de eerste lijn en vergelijken we die met de bevindingen van andere implementatieonderzoeken op dit terrein. We hebben de effectiviteit van het SLIMMER-programma onderzocht door middel van een gerandomiseerd gecontroleerd onderzoek. SLIMMER blijkt te leiden tot verbeteringen in klinische en metabole risicofactoren, voedinginname, beweging en kwaliteit van leven. Daarbij waren klinische effecten van ons programma groter dan die van de meeste andere preventieprogramma’s. Dit kan komen door de gedegen voorbereiding, het intensieve programma, het onderhoudsprogramma en aansluiting bij de reguliere werkwijze van eerstelijnszorgverleners. De resultaten van dit onderzoek bieden waardevolle inzichten die kunnen bijdragen aan structurele verankering en financiering van effectieve diabetespreventieprogramma’s in de Nederlandse eerste lijn

Bandwidth Improvement in Ultrasound Image Reconstruction Using Deep Learning Techniques

Ultrasound (US) imaging is a medical imaging modality that uses the reflection of sound in the range of 2–18 MHz to image internal body structures. In US, the frequency bandwidth (BW) is directly associated with image resolution. BW is a property of the transducer and more bandwidth comes at a higher cost. Thus, methods that can transform strongly bandlimited ultrasound data into broadband data are essential. In this work, we propose a deep learning (DL) technique to improve the image quality for a given bandwidth by learning features provided by broadband data of the same field of view. Therefore, the performance of several DL architectures and conventional state-of-the-art techniques for image quality improvement and artifact removal have been compared on in vitro US datasets. Two training losses have been utilized on three different architectures: a super resolution convolutional neural network (SRCNN), U-Net, and a residual encoder decoder network (REDNet) architecture. The models have been trained to transform low-bandwidth image reconstructions to high-bandwidth image reconstructions, to reduce the artifacts, and make the reconstructions visually more attractive. Experiments were performed for 20%, 40%, and 60% fractional bandwidth on the original images and showed that the improvements obtained are as high as 45.5% in RMSE, and 3.85 dB in PSNR, in datasets with a 20% bandwidth limitation

Bandwidth Improvement in Ultrasound Image Reconstruction Using Deep Learning Techniques

Ultrasound (US) imaging is a medical imaging modality that uses the reflection of sound in the range of 2–18 MHz to image internal body structures. In US, the frequency bandwidth (BW) is directly associated with image resolution. BW is a property of the transducer and more bandwidth comes at a higher cost. Thus, methods that can transform strongly bandlimited ultrasound data into broadband data are essential. In this work, we propose a deep learning (DL) technique to improve the image quality for a given bandwidth by learning features provided by broadband data of the same field of view. Therefore, the performance of several DL architectures and conventional state-of-the-art techniques for image quality improvement and artifact removal have been compared on in vitro US datasets. Two training losses have been utilized on three different architectures: a super resolution convolutional neural network (SRCNN), U-Net, and a residual encoder decoder network (REDNet) architecture. The models have been trained to transform low-bandwidth image reconstructions to high-bandwidth image reconstructions, to reduce the artifacts, and make the reconstructions visually more attractive. Experiments were performed for 20%, 40%, and 60% fractional bandwidth on the original images and showed that the improvements obtained are as high as 45.5% in RMSE, and 3.85 dB in PSNR, in datasets with a 20% bandwidth limitation

Automatic classification of focal liver lesions based on MRI and risk factors

\u3cp\u3eOBJECTIVES: Accurate classification of focal liver lesions is an important part of liver disease diagnostics. In clinical practice, the lesion type is often determined from the abdominal MR examination, which includes T2-weighted and dynamic contrast enhanced (DCE) MR images. To date, only T2-weighted images are exploited for automatic classification of focal liver lesions. In this study additional MR sequences and risk factors are used for automatic classification to improve the results and to make a step forward to a clinically useful aid for radiologists.\u3c/p\u3e\u3cp\u3eMATERIALS AND METHODS: Clinical MRI data sets of 95 patients with in total 125 benign lesions (40 adenomas, 29 cysts and 56 hemangiomas) and 88 malignant lesions (30 hepatocellular carcinomas (HCC) and 58 metastases) were included in this study. Contrast curve, gray level histogram, and gray level co-occurrence matrix texture features were extracted from the DCE-MR and T2-weighted images. In addition, risk factors including the presence of steatosis, cirrhosis, and a known primary tumor were used as features. Fifty features with the highest ANOVA F-score were selected and fed to an extremely randomized trees classifier. The classifier evaluation was performed using the leave-one-out principle and receiver operating characteristic (ROC) curve analysis.\u3c/p\u3e\u3cp\u3eRESULTS: The overall accuracy for the classification of the five major focal liver lesion types is 0.77. The sensitivity/specificity is 0.80/0.78, 0.93/0.93, 0.84/0.82, 0.73/0.56, and 0.62/0.77 for adenoma, cyst, hemangioma, HCC, and metastasis, respectively.\u3c/p\u3e\u3cp\u3eCONCLUSION: The proposed classification system using features derived from clinical DCE-MR and T2-weighted images, with additional risk factors is able to differentiate five common types of lesions and is a step forward to a clinically useful aid for focal liver lesion diagnosis.\u3c/p\u3