Robotic instrument segmentation with image-to-image translation

The semantic segmentation of robotic surgery video and the delineation of robotic instruments are important for enabling automation. Despite major recent progresses, the majority of the latest deep learning models for instrument detection and segmentation rely on large datasets with ground truth labels. While demonstrating the capability, reliance on large labelled data is a problem for practical applications because systems would need to be re-trained on domain variations such as procedure type or instrument sets. In this paper, we propose to alleviate this problem by training deep learning models on datasets that are synthesised using image-to-image translation techniques and we investigate different methods to perform this process optimally. Experimentally, we demonstrate that the same deep network architecture for robotic instrument segmentation can be trained on both real data and on our proposed synthetic data without affecting the quality of the output models' performance. We show this for several recent approaches and provide experimental support on publicly available datasets, which highlight the potential value of this approach

Targeting MAGE-C1/CT7 Expression Increases Cell Sensitivity to the Proteasome Inhibitor Bortezomib in Multiple Myeloma Cell Lines

The MAGE-C1/CT7 encodes a cancer/testis antigen (CTA), is located on the chromosomal region Xq26-27 and is highly polymorphic in humans. MAGE-C1/CT7 is frequently expressed in multiple myeloma (MM) that may be a potential target for immunotherapy in this still incurable disease. MAGEC1/CT7 expression is restricted to malignant plasma cells and it has been suggested that MAGE-C1/CT7 might play a pathogenic role in MM; however, the exact function this protein in the pathophysiology of MM is not yet understood. Our objectives were (1) to clarify the role of MAGE-C1/CT7 in the control of cellular proliferation and cell cycle in myeloma and (2) to evaluate the impact of silencing MAGE-C1/CT7 on myeloma cells treated with bortezomib. Myeloma cell line SKO-007 was transduced for stable expression of shRNA-MAGE-C1/CT7. Downregulation of MAGE-C1/CT7 was confirmed by real time quantitative PCR and western blot. Functional assays included cell proliferation, cell invasion, cell cycle analysis and apoptosis. Western blot showed a 70-80% decrease in MAGE-C1/CT7 protein expression in inhibited cells (shRNA-MAGE-C1/CT7) when compared with controls. Functional assays did not indicate a difference in cell proliferation and DNA synthesis when inhibited cells were compared with controls. However, we found a decreased percentage of cells in the G2/M phase of the cell cycle among inhibited cells, but not in the controls (p < 0.05). When myeloma cells were treated with bortezomib, we observed a 48% reduction of cells in the G2/M phase among inhibited cells while controls showed 13% (empty vector) and 9% (ineffective shRNA) reduction, respectively (p < 0.01). Furthermore, inhibited cells treated with bortezomib showed an increased percentage of apoptotic cells (Annexin V+/PI-) in comparison with bortezomib-treated controls (p < 0.001). We found that MAGE-C1/CT7 protects SKO-007 cells against bortezomib-induced apoptosis. Therefore, we could speculate that MAGE-C1/CT7 gene therapy could be a strategy for future therapies in MM, in particular in combination with proteasome inhibitors.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Laboratory of Molecular Biology and Genomics, Ludwig Institute for Cancer Research, São Paulo Branch, BrazilUniversidade Federal de São Paulo, Disciplina Hematol & Hemoterapia, São Paulo, BrazilLudwig Inst Canc Res, Lab Mol Biol & Genom, São Paulo, BrazilRecepta Biopharma, Ludwig Inst Canc Res, São Paulo, BrazilInCor, Fac Med, Setor Vetores Virais, Lab Genet & Cardiol Mol, São Paulo, BrazilJohns Hopkins Univ, Sch Med, Dept Neurosurg, Ludwig Collaborat Grp, Baltimore, MD 21205 USAUniv Med Ctr Hamburg Eppendorf, Dept Med 2, Hamburg, GermanyUniversidade Federal de São Paulo, Disciplina Hematol & Hemoterapia, São Paulo, BrazilWeb of Scienc

Trump vs. Hillary: What went Viral during the 2016 US Presidential Election

In this paper, we present quantitative and qualitative analysis of the top retweeted tweets (viral tweets) pertaining to the US presidential elections from September 1, 2016 to Election Day on November 8, 2016. For everyday, we tagged the top 50 most retweeted tweets as supporting or attacking either candidate or as neutral/irrelevant. Then we analyzed the tweets in each class for: general trends and statistics; the most frequently used hashtags, terms, and locations; the most retweeted accounts and tweets; and the most shared news and links. In all we analyzed the 3,450 most viral tweets that grabbed the most attention during the US election and were retweeted in total 26.3 million times accounting over 40% of the total tweet volume pertaining to the US election in the aforementioned period. Our analysis of the tweets highlights some of the differences between the social media strategies of both candidates, the penetration of their messages, and the potential effect of attacks on bothComment: Paper to appear in Springer SocInfo 201

Why Would the Rise of Social Media Increase the Influence of Traditional Media on Collective Judgments? A Response to Blevins and Ragozzino

In our original article (Etter, Ravasi & Colleoni, 2018), we argued that the rise of social media is changing how evaluations are made public and impact the formation of organizational reputation. In their counterpoint, [authors] argue in favour of a separation between the construct of media reputation and social media reputation. They further argue that the rise of social media is actually strengthening the impact of traditional media on the evaluations of key stakeholders. Finally, they urge scholars to take a cautious approach to the assumption that social media are introducing more dynamism in the formation of (media) reputation. We agree that, in some circumstances, a conceptual distinction between (traditional) media reputation and social media reputation might be useful to advance future research and theorization of reputational dynamics. In fact, in our original article we highlighted the importance to acknowledge the potential existence of different and separate “reputational arenas” (Aula & Mantere, 2013; see also Bromberg & Fine, 2002). We are less persuaded, however, by the other objections that [authors] raise

Adverse prognostic value of peritumoral vascular invasion: is it abrogated by adequate endocrine adjuvant therapy? Results from two International Breast Cancer Study Group randomized trials of chemoendocrine adjuvant therapy for early breast cancer

Background: Peritumoral vascular invasion (PVI) may assist in assigning optimal adjuvant systemic therapy for women with early breast cancer. Patients and methods: Patients participated in two International Breast Cancer Study Group randomized trials testing chemoendocrine adjuvant therapies in premenopausal (trial VIII) or postmenopausal (trial IX) node-negative breast cancer. PVI was assessed by institutional pathologists and/or central review on hematoxylin-eosin-stained slides in 99% of patients (analysis cohort 2754 patients, median follow-up >9 years). Results: PVI, present in 23% of the tumors, was associated with higher grade tumors and larger tumor size (trial IX only). Presence of PVI increased locoregional and distant recurrence and was significantly associated with poorer disease-free survival. The adverse prognostic impact of PVI in trial VIII was limited to premenopausal patients with endocrine-responsive tumors randomized to therapies not containing goserelin, and conversely the beneficial effect of goserelin was limited to patients whose tumors showed PVI. In trial IX, all patients received tamoxifen: the adverse prognostic impact of PVI was limited to patients with receptor-negative tumors regardless of chemotherapy. Conclusion: Adequate endocrine adjuvant therapy appears to abrogate the adverse impact of PVI in node-negative disease, while PVI may identify patients who will benefit particularly from adjuvant therap

Adverse prognostic value of peritumoral vascular invasion: is it abrogated by adequate endocrine adjuvant therapy? Results from two International Breast Cancer Study Group randomized trials of chemoendocrine adjuvant therapy for early breast cancer

Background: Peritumoral vascular invasion (PVI) may assist in assigning optimal adjuvant systemic therapy for women with early breast cancer. Patients and methods: Patients participated in two International Breast Cancer Study Group randomized trials testing chemoendocrine adjuvant therapies in premenopausal (trial VIII) or postmenopausal (trial IX) node-negative breast cancer. PVI was assessed by institutional pathologists and/or central review on hematoxylin-eosin-stained slides in 99% of patients (analysis cohort 2754 patients, median follow-up >9 years). Results: PVI, present in 23% of the tumors, was associated with higher grade tumors and larger tumor size (trial IX only). Presence of PVI increased locoregional and distant recurrence and was significantly associated with poorer disease-free survival. The adverse prognostic impact of PVI in trial VIII was limited to premenopausal patients with endocrine-responsive tumors randomized to therapies not containing goserelin, and conversely the beneficial effect of goserelin was limited to patients whose tumors showed PVI. In trial IX, all patients received tamoxifen: the adverse prognostic impact of PVI was limited to patients with receptor-negative tumors regardless of chemotherapy. Conclusion: Adequate endocrine adjuvant therapy appears to abrogate the adverse impact of PVI in node-negative disease, while PVI may identify patients who will benefit particularly from adjuvant therap

Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery

Semantic tool segmentation in surgical videos is important for surgical scene understanding and computer-assisted interventions as well as for the development of robotic automation. The problem is challenging because different illumination conditions, bleeding, smoke and occlusions can reduce algorithm robustness. At present labelled data for training deep learning models is still lacking for semantic surgical instrument segmentation and in this paper we show that it may be possible to use robot kinematic data coupled with laparoscopic images to alleviate the labelling problem. We propose a new deep learning based model for parallel processing of both laparoscopic and simulation images for robust segmentation of surgical tools. Due to the lack of laparoscopic frames annotated with both segmentation ground truth and kinematic information a new custom dataset was generated using the da Vinci Research Kit (dVRK) and is made available

CA15-3 and alkaline phosphatase as predictors for breast cancer recurrence: a combined analysis of seven International Breast Cancer Study Group trials

Background: We evaluated the ability of CA15-3 and alkaline phosphatase (ALP) to predict breast cancer recurrence. Patients and methods: Data from seven International Breast Cancer Study Group trials were combined. The primary end point was relapse-free survival (RFS) (time from randomization to first breast cancer recurrence), and analyses included 3953 patients with one or more CA15-3 and ALP measurement during their RFS period. CA15-3 was considered abnormal if >30 U/ml or >50% higher than the first value recorded; ALP was recorded as normal, abnormal, or equivocal. Cox proportional hazards models with a time-varying indicator for abnormal CA15-3 and/or ALP were utilized. Results: Overall, 784 patients (20%) had a recurrence, before which 274 (35%) had one or more abnormal CA15-3 and 35 (4%) had one or more abnormal ALP. Risk of recurrence increased by 30% for patients with abnormal CA15-3 [hazard ratio (HR) = 1.30; P = 0.0005], and by 4% for those with abnormal ALP (HR = 1.04; P = 0.82). Recurrence risk was greatest for patients with either (HR = 2.40; P < 0.0001) and with both (HR = 4.69; P < 0.0001) biomarkers abnormal. ALP better predicted liver recurrence. Conclusions: CA15-3 was better able to predict breast cancer recurrence than ALP, but use of both biomarkers together provided a better early indicator of recurrence. Whether routine use of these biomarkers improves overall survival remains an open questio

Influence of Textile Structure and Silica Based Finishing on Thermal Insulation Properties of Cotton Fabrics

The aim of this work is to investigate the influence of weave structures and silica coatings obtained via sol-gel process on the thermal insulation properties of cotton samples. For this reason three main weave structures (plain, satin, and piqué) of cotton fabric were selected with different yarn count, threads per cm, and mass per square meter values. Thereafter, only for the plain weave, the samples were padded using silica sol formed by hydrolysis and subsequent condensation of 3-glycidoxypropyltrimethoxysilane under acidic conditions. The silanized plain weave samples were characterized by TGA and FT-IR techniques. The thermal properties were measured with a home-made apparatus in order to calculate thermal conductivity, resistance, and absorption of all the treated fabric samples. The relationship between the thermal insulation properties of the plain weave fabrics and the concentration of sol solutions has been investigated. Fabrics weave and density were found to strongly influence the thermal properties: piqué always shows the lowest values and satin shows the highest values while plain weave lies in between. The thermal properties of treated high-density cotton plain weave fabric were proved to be strongly influenced by finishing agent concentration

Deep Learning Based Robotic Tool Detection and Articulation Estimation with Spatio-Temporal Layers

Surgical-tool joint detection from laparoscopic images is an important but challenging task in computer-assisted minimally invasive surgery. Illumination levels, variations in background and the different number of tools in the field of view, all pose difficulties to algorithm and model training. Yet, such challenges could be potentially tackled by exploiting the temporal information in laparoscopic videos to avoid per frame handling of the problem. In this letter, we propose a novel encoder-decoder architecture for surgical instrument joint detection and localization that uses three-dimensional convolutional layers to exploit spatio-temporal features from laparoscopic videos. When tested on benchmark and custom-built datasets, a median Dice similarity coefficient of 85.1% with an interquartile range of 4.6% highlights performance better than the state of the art based on single-frame processing. Alongside novelty of the network architecture, the idea for inclusion of temporal information appears to be particularly useful when processing images with unseen backgrounds during the training phase, which indicates that spatio-temporal features for joint detection help to generalize the solution