Feature Aggregation Decoder for Segmenting Laparoscopic Scenes

Laparoscopic scene segmentation is one of the key building blocks required for developing advanced computer assisted interventions and robotic automation. Scene segmentation approaches often rely on encoder-decoder architectures that encode a representation of the input to be decoded to semantic pixel labels. In this paper, we propose to use the deep Xception model for the encoder and a simple yet effective decoder that relies on a feature aggregation module. Our feature aggregation module constructs a mapping function that reuses and transfers encoder features and combines information across all feature scales to build a richer representation that keeps both high-level context and low-level boundary information. We argue that this aggregation module enables us to simplify the decoder and reduce the number of parameters in the decoder. We have evaluated our approach on two datasets and our experimental results show that our model outperforms state-of-the-art models on the same experimental setup and significantly improves the previous results, 98.44% vs 89.00% , on the EndoVis’15 dataset

EasyLabels: weak labels for scene segmentation in laparoscopic videos

PURPOSE: We present a different approach for annotating laparoscopic images for segmentation in a weak fashion and experimentally prove that its accuracy when trained with partial cross-entropy is close to that obtained with fully supervised approaches. METHODS: We propose an approach that relies on weak annotations provided as stripes over the different objects in the image and partial cross-entropy as the loss function of a fully convolutional neural network to obtain a dense pixel-level prediction map. RESULTS: We validate our method on three different datasets, providing qualitative results for all of them and quantitative results for two of them. The experiments show that our approach is able to obtain at least [Formula: see text] of the accuracy obtained with fully supervised methods for all the tested datasets, while requiring [Formula: see text][Formula: see text] less time to create the annotations compared to full supervision. CONCLUSIONS: With this work, we demonstrate that laparoscopic data can be segmented using very few annotated data while maintaining levels of accuracy comparable to those obtained with full supervision

Automated operative workflow analysis of endoscopic pituitary surgery using machine learning: development and preclinical evaluation (IDEAL stage 0)

OBJECTIVE: Surgical workflow analysis involves systematically breaking down operations into key phases and steps. Automatic analysis of this workflow has potential uses for surgical training, preoperative planning, and outcome prediction. Recent advances in machine learning (ML) and computer vision have allowed accurate automated workflow analysis of operative videos. In this Idea, Development, Exploration, Assessment, Long-term study (IDEAL) stage 0 study, the authors sought to use Touch Surgery for the development and validation of an ML-powered analysis of phases and steps in the endoscopic transsphenoidal approach (eTSA) for pituitary adenoma resection, a first for neurosurgery. METHODS: The surgical phases and steps of 50 anonymized eTSA operative videos were labeled by expert surgeons. Forty videos were used to train a combined convolutional and recurrent neural network model by Touch Surgery. Ten videos were used for model evaluation (accuracy, F1 score), comparing the phase and step recognition of surgeons to the automatic detection of the ML model. RESULTS: The longest phase was the sellar phase (median 28 minutes), followed by the nasal phase (median 22 minutes) and the closure phase (median 14 minutes). The longest steps were step 5 (tumor identification and excision, median 17 minutes); step 3 (posterior septectomy and removal of sphenoid septations, median 14 minutes); and step 4 (anterior sellar wall removal, median 10 minutes). There were substantial variations within the recorded procedures in terms of video appearances, step duration, and step order, with only 50% of videos containing all 7 steps performed sequentially in numerical order. Despite this, the model was able to output accurate recognition of surgical phases (91% accuracy, 90% F1 score) and steps (76% accuracy, 75% F1 score). CONCLUSIONS: In this IDEAL stage 0 study, ML techniques have been developed to automatically analyze operative videos of eTSA pituitary surgery. This technology has previously been shown to be acceptable to neurosurgical teams and patients. ML-based surgical workflow analysis has numerous potential uses-such as education (e.g., automatic indexing of contemporary operative videos for teaching), improved operative efficiency (e.g., orchestrating the entire surgical team to a common workflow), and improved patient outcomes (e.g., comparison of surgical techniques or early detection of adverse events). Future directions include the real-time integration of Touch Surgery into the live operative environment as an IDEAL stage 1 (first-in-human) study, and further development of underpinning ML models using larger data sets

Opioids Switching with Transdermal Systems in Chronic Cancer Pain

<p>Abstract</p> <p>Background</p> <p>Due to tolerance development and adverse side effects, chronic pain patients frequently need to be switched to alternative opioid therapy</p> <p>Objective</p> <p>To assess the efficacy and tolerability of an alternative transdermally applied (TDS) opioid in patients with chronic cancer pain receiving insufficient analgesia using their present treatment.</p> <p>Methods</p> <p>A total of 32 patients received alternative opioid therapy, 16 were switched from buprenorphine to fentanyl and 16 were switched from fentanyl to buprenorphine. The dosage used was 50% of that indicated in equipotency conversion tables. Pain relief was assessed at weekly intervals for the next 3 weeks</p> <p>Results</p> <p>Pain relief as assessed by VAS, PPI, and PRI significantly improved (p < 0.0001) in all patients at all 3 follow up visits. After 3 weeks of treatment, the reduction in the mean VAS, PPI, and PRI scores in the fentanyl and buprenorphine groups was 68, 77, 74, and 69, 79, and 62%, respectively. Over the same time period the use of oral morphine as rescue medication was reduced from 27.5 ± 20.5 (mean ± SD) to 3.75 ± 8.06, and 33.8 ± 18.9 to 3.75 ± 10.9 mg/day in the fentanyl and buprenorphine groups, respectively. There was no significant difference in either pain relief or rescue medication use between the two patient groups The number of patient with adverse events fell during the study. After the third week of the treatment the number of patients with constipation was reduced from 11 to 5, and 10 to 4 patients in the fentanyl and buprenorphine groups, respectively. There was a similar reduction in the incidence of nausea and vomiting. No sedation was seen in any patient after one week of treatment.</p> <p>Conclusion</p> <p>Opioid switching at 50% of the calculated equianalgesic dose produced a significant reduction in pain levels and rescue medication. The incidence of side effects decreased and no new side effects were noted. Further studies are required to provide individualized treatment for patients according to their different types of cancer.</p

Efeito das mudanças climáticas sobre a aptidão climática para cana-de-açúcar no Estado de São Paulo.

Neste artigo, avaliou-se os impactos do aumento da temperatura no zoneamento de aptidão climática para cana-de-açúcar no Estado de São Paulo, baseando-se no quarto relatório do IPCC no que se refere as previsões de temperatura do ar e admitindo que o regime de chuvas fosse mantido

Genome-Wide Progesterone Receptor Binding: Cell Type-Specific and Shared Mechanisms in T47D Breast Cancer Cells and Primary Leiomyoma Cells

Progesterone, via its nuclear receptor (PR), exerts an overall tumorigenic effect on both uterine fibroid (leiomyoma) and breast cancer tissues, whereas the antiprogestin RU486 inhibits growth of these tissues through an unknown mechanism. Here, we determined the interaction between common or cell-specific genome-wide binding sites of PR and mRNA expression in RU486-treated uterine leiomyoma and breast cancer cells.ChIP-sequencing revealed 31,457 and 7,034 PR-binding sites in breast cancer and uterine leiomyoma cells, respectively; 1,035 sites overlapped in both cell types. Based on the chromatin-PR interaction in both cell types, we statistically refined the consensus progesterone response element to G•ACA• • •TGT•C. We identified two striking differences between uterine leiomyoma and breast cancer cells. First, the cis-regulatory elements for HSF, TEF-1, and C/EBPα and β were statistically enriched at genomic RU486/PR-targets in uterine leiomyoma, whereas E2F, FOXO1, FOXA1, and FOXF sites were preferentially enriched in breast cancer cells. Second, 51.5% of RU486-regulated genes in breast cancer cells but only 6.6% of RU486-regulated genes in uterine leiomyoma cells contained a PR-binding site within 5 kb from their transcription start sites (TSSs), whereas 75.4% of RU486-regulated genes contained a PR-binding site farther than 50 kb from their TSSs in uterine leiomyoma cells. RU486 regulated only seven mRNAs in both cell types. Among these, adipophilin (PLIN2), a pro-differentiation gene, was induced via RU486 and PR via the same regulatory region in both cell types.Our studies have identified molecular components in a RU486/PR-controlled gene network involved in the regulation of cell growth, cell migration, and extracellular matrix function. Tissue-specific and common patterns of genome-wide PR binding and gene regulation may determine the therapeutic effects of antiprogestins in uterine fibroids and breast cancer

Novel nanohydrogel of hyaluronic acid loaded with quercetin alone and in combination with temozolomide as new therapeutic tool, CD44 targeted based, of glioblastoma multiforme

Glioblastoma multiforme is the most common and aggressive primary brain cancer with only ∼3% of patients surviving more than 3 years from diagnosis. Several mechanisms are involved in drug and radiation resistance to anticancer treatments and among them one of the most important factors is the tumour microenvironment status, characterised by cancer cell hypersecretion of interleukins and cytokines. The aim of our research was the synthesis of a nanocarrier of quercetin combined with temozolomide, to enhance the specificity and efficacy of this anticancer drug commonly used in glioblastoma treatment. The nanohydrogel increased the internalization and cytotoxicity of quercetin in human glioblastoma cells and, when co-delivered with temozolomide, contribute to an improved anticancer effect. The nanohydrogel loaded with quercetin had the ability to recognize CD44 receptor, a brain cancer cell marker, through an energy and caveolae dependent mechanism of internalization. Moreover, nanohydrogel of quercetin was able to reduce significantly IL-8, IL-6 and VEGF production in pro-inflammatory conditions with interesting implications on the mechanism of glioblastoma cells drug resistance. In summary, novel CD44 targeted polymeric based nanocarriers appear to be proficient in mediating site-specific delivery of quercetin via CD44 receptor in glioblastoma cells. This targeted therapy lead to an improved therapeutic efficacy of temozolomide by modulating the brain tumour microenvironment