Monocular Microscope to CT Registration using Pose Estimation of the Incus for Augmented Reality Cochlear Implant Surgery

For those experiencing severe-to-profound sensorineural hearing loss, the cochlear implant (CI) is the preferred treatment. Augmented reality (AR) aided surgery can potentially improve CI procedures and hearing outcomes. Typically, AR solutions for image-guided surgery rely on optical tracking systems to register pre-operative planning information to the display so that hidden anatomy or other important information can be overlayed and co-registered with the view of the surgical scene. In this paper, our goal is to develop a method that permits direct 2D-to-3D registration of the microscope video to the pre-operative Computed Tomography (CT) scan without the need for external tracking equipment. Our proposed solution involves using surface mapping of a portion of the incus in surgical recordings and determining the pose of this structure relative to the surgical microscope by performing pose estimation via the perspective-n-point (PnP) algorithm. This registration can then be applied to pre-operative segmentations of other anatomy-of-interest, as well as the planned electrode insertion trajectory to co-register this information for the AR display. Our results demonstrate the accuracy with an average rotation error of less than 25 degrees and a translation error of less than 2 mm, 3 mm, and 0.55% for the x, y, and z axes, respectively. Our proposed method has the potential to be applicable and generalized to other surgical procedures while only needing a monocular microscope during intra-operation

Adaptable image quality assessment using meta-reinforcement learning of task amenability

The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjective (human-based) image quality assessment (IQA), task amenability potentially provides an objective measure of task-specific image quality. To predict task amenability, an IQA agent is trained using reinforcement learning (RL) with a simultaneously optimised task predictor, such as a classification or segmentation neural network. In this work, we develop transfer learning or adaptation strategies to increase the adaptability of both the IQA agent and the task predictor so that they are less dependent on high-quality, expert-labelled training data. The proposed transfer learning strategy re-formulates the original RL problem for task amenability in a meta-reinforcement learning (meta-RL) framework. The resulting algorithm facilitates efficient adaptation of the agent to different definitions of image quality, each with its own Markov decision process environment including different images, labels and an adaptable task predictor. Our work demonstrates that the IQA agents pre-trained on non-expert task labels can be adapted to predict task amenability as defined by expert task labels, using only a small set of expert labels. Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19.7 % % and 29.6 % % expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100 % % expert labels

Observation of Plasma Focusing of a 28.5 GeV Positron Beam

The observation of plasma focusing of a 28.5 GeV positron beam is reported. The plasma was formed by ionizing a nitrogen jet only 3 mm thick. Simultaneous focusing in both transverse dimensions was observed with effective focusing strengths of order Tesla per micron. The minimum area of the beam spot was reduced by a factor of 2.0 +/- 0.3 by the plasma. The longitudinal beam envelope was measured and compared with numerical calculations

Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration

The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations. This work describes a fully 3D prototypical few-shot segmentation algorithm, such that the trained networks can be effectively adapted to clinically interesting structures that are absent in training, using only a few labelled images from a different institute. First, to compensate for the widely recognised spatial variability between institutions in episodic adaptation of novel classes, a novel spatial registration mechanism is integrated into prototypical learning, consisting of a segmentation head and an spatial alignment module. Second, to assist the training with observed imperfect alignment, support mask conditioning module is proposed to further utilise the annotation available from the support images. Extensive experiments are presented in an application of segmenting eight anatomical structures important for interventional planning, using a data set of 589 pelvic T2-weighted MR images, acquired at seven institutes. The results demonstrate the efficacy in each of the 3D formulation, the spatial registration, and the support mask conditioning, all of which made positive contributions independently or collectively. Compared with the previously proposed 2D alternatives, the few-shot segmentation performance was improved with statistical significance, regardless whether the support data come from the same or different institutes

Rates, predictive factors and effectiveness of ustekinumab intensification to 4- or 6-weekly intervals in Crohn's disease

Background: The UNITI trial reports efficacy of ustekinumab (UST) dose intensification in Crohn's disease (CD) from 12- to 8-weekly, but not 4-weekly. We aimed 1) to assess the cumulative incidence of UST dose intensification to 4- or 6-weekly, 2) to identify factors associated with dose intensification, and 3) to assess the effectiveness of this strategy. Methods: We performed a retrospective, observational cohort study in NHS Lothian including all UST treated CD patients (2015–2020). Results: 163 CD patients were treated with UST (median follow-up: 20.3 months [13.4–38.4]), of whom 55 (33.7%) underwent dose intensification to 4-weekly (n = 50, 30.7%) or 6-weekly (n = 5, 3.1%). After 1 year 29.9% were dose intensified. Prior exposure to both anti-TNF and vedolizumab (HR 9.5; 1.3–70.9), and concomitant steroid use at UST start (HR 1.8; 1.0–3.1) were associated with dose intensification. Following dose intensification, 62.6% patients (29/55) remained on UST beyond 1 year. Corticosteroid-free clinical remission was achieved in 27% at week 16 and 29.6% at last follow-up. Conclusion: One third of CD patients treated with UST underwent dose intensification to a 4- or 6-weekly interval within the first year. Patients who failed both anti-TNF and vedolizumab, or required steroids at initiation were more likely to dose intensify.</p

Online Hate: From the Far-Right to the ‘Alt-Right’, and from the Margins to the Mainstream

In the 1990s and early 2000s, there was much discussion about the democratic and anti-democratic implications of the Internet. The latter particularly focused on the ways in which the far-right were using the Internet to spread hate and recruit members. Despite this common assumption, the American far-right did not harness the Internet quickly, effectively or widely. More recently, however, they have experienced a resurgence and mainstreaming, benefitting greatly from social media. This chapter examines the history of their use of the Internet with respect to: (1) how this developed in response to political changes and emerging technologies; (2) how it reflected and changed the status of such movements and their brand of hate; and (3) the relationship between online activity and traditional methods of communication

Synthesis of Dihydroindoloisoquinolines through Copper‐Catalyzed Cross‐Dehydrogenative Coupling of Tetrahydroisoquinolines and Nitroalkanes

Lately, the cross‐dehydrogenative coupling of tetrahydroisoquinolines and nitroalkanes has become a widely studied reaction in organic chemistry; the corresponding β‐nitroamines are generally formed irrespective of the catalysis and activation mode utilized. A quite distinct behavior was observed when the reaction was catalyzed by copper nanoparticles supported on titania, leading to the formation of 5,6‐dihydroindolo[2,1‐a]isoquinolines with high selectivity and good yields. A meticulous reaction mechanism is proposed, based on experimentation, and discussed along with a key chemical modification of these compounds. Apparently, the catalyst effectiveness resides in its nanostructured character, outperforming the activity of the commercial copper catalysts.This work was generously supported by the Spanish Ministerio de Economía y Competitividad (MINECO; grant no. CTQ2017-88171-P), the Generalitat Valenciana (GV; grant no. AICO/2017/007), and the Instituto de Síntesis Orgánica (ISO). I.M.-G. thanks the Vicerrectorado de Investigación y Transferencia del Conocimiento of the Universidad de Alicante for a pre-doctoral grant (no. UAFPU2016-034)

Effectiveness and Safety of Adalimumab Biosimilar SB5 in IBD:Outcomes in Originator to SB5 Switch, Double Biosimilar Switch and Bio-Naieve SB5 Observational Cohorts

BACKGROUND AND AIMS: Multiple adalimumab [ADA] biosimilars are now approved for use in inflammatory bowel disease [IBD]; however, effectiveness and safety data remain scarce. We aimed to investigate long-term outcomes of the ADA biosimilar SB5 in IBD patients following a switch from the ADA originator [SB5-switch cohort] or after start of SB5 [SB5-start cohort]. METHODS: We performed an observational cohort study in a tertiary IBD referral centre. All IBD patients treated with Humira underwent an elective switch to SB5. We identified all these patients in a biological prescription database that prospectively registered all ADA start and stop dates including brand names. Data on IBD phenotype, C-reactive protein [CRP], drug persistence, ADA drug and antibody levels, and faecal calprotectin were collected. RESULTS: In total, 481 patients were treated with SB5, 256 in the SB5-switch cohort (median follow-up: 13.7 months [IQR 8.6–15.2]) and 225 in the SB5-start cohort [median follow-up: 8.3 months [4.2–12.8]). Of the SB5-switch cohort, 70.8% remained on SB5 beyond 1 year; 90/256 discontinued SB5, mainly due to adverse events [46/90] or secondary loss of response [37/90]. In the SB5-start cohort, 81/225 discontinued SB5, resulting in SB5-drug persistence of 60.3% beyond 1 year. No differences in clinical remission [p = 0.53], CRP [p = 0.80], faecal calprotectin [p = 0.40] and ADA trough levels [p = 0.55] were found between baseline, week 26 and week 52 following switch. Injection site pain was the most frequently reported adverse event. CONCLUSION: Switching from ADA originator to SB5 appeared effective and safe in this study with over 12 months of follow-up

Automatic Detection and Classification of Breast Tumors in Ultrasonic Images Using Texture and Morphological Features

Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity