Minimally invasive surgery like laparoscopic surgery is an active research area of clinical practice for less pain and a faster recovery rate. Detection of surgical tools with more accurate spatial locations in surgical videos not only helps to ensure patient safety by reducing the incidence of complications but also makes a difference to assess the surgeon performance. In this paper, we propose a novel Modulated Anchoring Network for detection of laparoscopic surgery tools based on Faster R-CNN, which inherits the merits of two-stage approaches while also maintains high efficiency of comparable speed as state-of-the-art one-stage methods. Since objects like surgical instruments with a wide aspect ratio are difficult to recognize, we develop a novel training scheme named as modulated anchoring to explicitly predict arbitrary anchor shapes of objects of interest. For taking the relationship of different tools into consideration, it is useful to embed the relation module in our network. We evaluate our method using an existing dataset (m2cai16-tool-locations) and a new private dataset (AJU-Set), both collected from cholecystectomy surgical videos in hospital, covering information of seven surgical tools with spatial bounds. We show that our detector yields excellent detection accuracy of 69.6% and 76.5% over the introduced datasets superior to other recently used architectures. We further verify the efficiency of our method by analyzing the usage patterns of tools, the economy of the movement, and the dexterity of operations to assess surgical quality.

Accurate segmentation of lesion region from skin lesion images can provide guidance for medical experts to make an early diagnosis of skin cancer. In this study, we construct Recurrent Attentional Convolutional Networks (O-Net), which exploits the skin lesion’s attention class feature with a recurrent O-shape structure, in an iterative refinement strategy for skin lesion image segmentation. Inspired by the recently proposed attention class feature network, we integrate the attention class feature module into the proposed networks. The O-Net, with recurrent unit to iteratively refine the segmentation result, is designed to extract attention feature information and enable coarse-to-fine feature representation by iteratively integrating attention feature maps into network. Furthermore, O-Net calculates the attentional class feature by extracting attention information from the coarse segmentation result. Two currently popular datasets ISIC-2017 and PH2 are employed to explore the validity of our proposed model. The study provides detailed comparisons of our proposed network, the attention class feature network and Recurrent U-Net. O-Net achieved Dice coefficient by 87.04% on the ISIC-2017 dataset, 92.12% on the PH2 dataset with corresponding Jaccard indices of 80.36% and 86.15%, respectively, on the same dataset, which exhibits competitive performance for skin lesion image segmentation in results. The visual results also shown that more detailed tissues are extracted by O-Net than other methods.

With the constant changes of people’s lifestyle and living environment, the morbidity of breast cancer is increasing year by year. It is highly imperative to develop an effective breast mass segmentation method for early breast cancer diagnosis. However, segmenting breast masses in mammograms is still one hot issue with enormous challenges because of masses’ irregular shapes and various sizes. In this study, we propose multi-adversarial learning to capture multi-scale image information for accurate breast mass segmentation. To effectively reinforce higher-order consistency in the segmentation results, the proposed network introduces the idea of adversarial networks, mainly consisting of a segmentation network and a discrimination network. An improved U-Net is introduced as the segmentation network to generate masks of the suspicious regions, while the discrimination network combines three convolutional critic networks that operate at different scales to discriminate the input masks. To weaken the unbalanced class problem and produce fine-grained segmentation results, weighted cross entropy loss and Earth-Mover distance are jointly used as an integrated loss function to guide the optimization process. Furthermore, the spectral normalization is adopted to the critics to alleviate the instability of training. The effectiveness of the proposed method is evaluated on two public datasets (INbreast and CBIS-DDSM). Experimental results empirically demonstrate that our method outperforms FCNs-based methods and the state-of-the-art method with dice of 81.64% for INbreast and 82.16% for CBIS-DDSM.

Maternal allergic disease history and impaired regulatory T-cells (Tregs) are critical risk factors for allergy development in children. However, the mechanisms that underlie these risk factors remain poorly defined. Therefore, the aim of this study was to assess whether maternal allergies affect the Tregs of offspring and lead to allergy development in childhood. A total of 332 mothers of healthy newborns (234 from no allergic mothers, 98 from allergic mothers) were recruited to this study. Detailed questionnaires were administered yearly to determine the allergy status of the mothers and the newborns from birth to 3 years of age. Cord blood samples obtained at the time of birth were analysed for Treg counts, as well Treg activity, based on their response to Toll-like receptor (TLR) stimuli such as lipid A (LPA) and peptidoglycans (PPG). Surface markers, associated genes, suppressive capacity, and cytokine levels of Tregs were also measured. Possible correlations between Treg activity and maternal or neonate allergies were assessed. In addition, environmental microbial content and other known risk factors for allergies were measured. Cord blood mononuclear cells (CBMCs) from offspring with allergic mothers showed fewer CD4+CD25+FOXP3+ T cells, lower expression levels of associated genes, and reduced cytokine production of interleukin (IL)-10 and interferon-γ (P < 0.05), especially via the PPG-TLR2 pathway. Suppression of effector T cells by Tregs from children of mothers with allergies was impaired, especially IL-13 production by Type 2 T helper (Th2) cells (P = 0.026). Children who developed allergies in the first 3 years of life had lower numbers of CD4+CD25+FOXP3+ T cells and reduced FOXP3 expression and IL-10 production as newborns (P < 0.05). Maternal allergic background was identified as a risk factor for allergy development in the children (Odds ratio (OR) = 2.46, 95 % CI = 1.05–5.79); while declining Treg numbers, IL-10 production, and FOXP3 expression in neonates (PPG and LPA stimulated) were identified as independent risk factors for allergic diseases in offspring at 3 years of age after adjusting for maternal allergic history and environmental factors (P < 0.05). Maternal allergy correlated with impaired Tregs in neonates, and this could enhance the susceptibility of offspring to allergic diseases in early childhood due to an imbalance of Th1 and Th2 cells.

We have developed a novel lipase-mediated method to realize the Dakin reaction. A wide range of hydroxylated benzaldehydes could be oxidized with high yields (from 90% to 97%) under mild reaction conditions. Moreover, this lipase-mediated reaction could be scaled up easily and Novozym 435 could be reused more than 10 runs without an obvious decrease in enzyme activity. This protocol expands the application of lipase in organic synthesis and offers a complementary route for the Dakin reaction. GRAPHICAL ABSTRACT