Point Projection Mapping System for Tracking, Registering, Labeling and Validating Optical Tissue Measurements

Validation of newly developed optical tissue sensing techniques for tumor detection during cancer surgery requires an accurate correlation with histological results. Additionally, such accurate correlation facilitates precise data labeling for developing high-performance machine-learning tissue classification models. In this paper, a newly developed Point Projection Mapping system will be introduced, which allows non-destructive tracking of the measurement locations on tissue specimens. Additionally, a framework for accurate registration, validation, and labeling with histopathology results is proposed and validated on a case study. The proposed framework provides a more robust and accurate method for tracking and validation of optical tissue sensing techniques, which saves time and resources compared to conventional techniques available

Durability and wear resistance of laser-textured hardened stainless steel surfaces with hydrophobic properties

Hydrophobic surfaces are of high interest to industry. While surface functionalization has attracted significant interest, from both industry and research, the durability of engineered surfaces remains a challenge, as wear and scratches deteriorate their functional response. In this work, a cost-effective combination of surface engineering processes on stainless steel was investigated. Low-temperature plasma surface alloying was applied to increase surface hardness from 172 to 305 HV. Then, near-infrared nanosecond laser patterning was deployed to fabricate channel-like patterns that enabled superhydrophobicity. Abrasion tests were carried out to examine the durability of such engineered surfaces during daily use. In particular, the evolution of surface topographies, chemical composition, and water contact angle with increasing abrasion cycles were studied. Hydrophobicity deteriorated progressively on both hardened and raw stainless steel samples, suggesting that the major contributing factor to hydrophobicity was the surface chemical composition. At the same time, samples with increased surface hardness exhibited a slower deterioration of their topographies when compared with nontreated surfaces. A conclusion is made about the durability of laser-textured hardened stainless steel surfaces produced by applying the proposed combined surface engineering approach

Discerning natural and anthropogenic organic matter inputs to salt marsh sediments of Ria Formosa lagoon (South Portugal)

Sedimentary organic matter (OM) origin and molecular composition provide useful information to understand carbon cycling in coastal wetlands. Core sediments from threors' Contributionse transects along Ria Formosa lagoon intertidal zone were analysed using analytical pyrolysis (Py-GC/MS) to determine composition, distribution and origin of sedimentary OM. The distribution of alkyl compounds (alkanes, alkanoic acids and alkan-2-ones), polycyclic aromatic hydrocarbons (PAHs), lignin-derived methoxyphenols, linear alkylbenzenes (LABs), steranes and hopanes indicated OM inputs to the intertidal environment from natural-autochthonous and allochthonous-as well as anthropogenic. Several n-alkane geochemical indices used to assess the distribution of main OM sources (terrestrial and marine) in the sediments indicate that algal and aquatic macrophyte derived OM inputs dominated over terrigenous plant sources. The lignin-derived methoxyphenol assemblage, dominated by vinylguaiacol and vinylsyringol derivatives in all sediments, points to large OM contribution from higher plants. The spatial distributions of PAHs (polyaromatic hydrocarbons) showed that most pollution sources were mixed sources including both pyrogenic and petrogenic. Low carbon preference indexes (CPI > 1) for n-alkanes, the presence of UCM (unresolved complex mixture) and the distribution of hopanes (C-29-C-36) and steranes (C-27-C-29) suggested localized petroleum-derived hydrocarbon inputs to the core sediments. Series of LABs were found in most sediment samples also pointing to domestic sewage anthropogenic contributions to the sediment OM.EU Erasmus Mundus Joint Doctorate fellowship (FUECA, University of Cadiz, Spain)EUEuropean Commission [FP7-ENV-2011, 282845, FP7-534 ENV-2012, 308392]MINECO project INTERCARBON [CGL2016-78937-R]info:eu-repo/semantics/publishedVersio

Point Projection Mapping System for Tracking, Registering, Labeling, and Validating Optical Tissue Measurements

The validation of newly developed optical tissue-sensing techniques for tumor detection during cancer surgery requires an accurate correlation with the histological results. Additionally, such an accurate correlation facilitates precise data labeling for developing high-performance machine learning tissue-classification models. In this paper, a newly developed Point Projection Mapping system will be introduced, which allows non-destructive tracking of the measurement locations on tissue specimens. Additionally, a framework for accurate registration, validation, and labeling with the histopathology results is proposed and validated on a case study. The proposed framework provides a more-robust and accurate method for the tracking and validation of optical tissue-sensing techniques, which saves time and resources compared to the available conventional techniques

Feasibility of Ex Vivo Margin Assessment with Hyperspectral Imaging during Breast-Conserving Surgery: From Imaging Tissue Slices to Imaging Lumpectomy Specimen

Developing algorithms for analyzing hyperspectral images as an intraoperative tool for margin assessment during breast-conserving surgery requires a dataset with reliable histopathologic labels. The feasibility of using tissue slices hyperspectral dataset with a high correlation with histopathology for developing an algorithm for analyzing the images from the surface of lumpectomy specimens was investigated. We presented a method to acquire hyperspectral images from the lumpectomy surface with a high correlation with histopathology. The tissue slices dataset was compared with the dataset obtained on lumpectomy specimen and the wavelengths with a penetration depth up to the minimum sample thickness of the tissue slices were used to develop a tissue classification algorithm. Spectral differences were observed between tissue slices and lumpectomy datasets due to differences in the sample thickness between both datasets; wavelengths with a high penetration depth were able to penetrate through the thinner tissue slices, affecting the captured signal. By using only wavelengths with a penetration depth up to the minimum sample thickness of the tissue slices, the adipose tissue could be discriminated from other tissue types, but differentiating malignant from connective tissue was more challenging

An improved U-net architecture for simultaneous arteriole and venule segmentation in fundus image

\u3cp\u3eThe segmentation and classification of retinal arterioles and venules play an important role in the diagnosis of various eye diseases and systemic diseases. The major challenges include complicated vessel structure, inhomogeneous illumination, and large background variation across subjects. In this study, we proposed an improved fully convolutional network that simultaneously segment arterioles and venules directly from the retinal image. To simultaneously segment retinal arterioles and venules, we configured the fully convolutional network to allow true color image as input and multiple labels as output. A domain-specific loss function is designed to improve the performance. The proposed method was assessed extensively on public datasets and compared with the state-of-the-art methods in literatures. The sensitivity and specificity of overall vessel segmentation on DRIVE is 0.870 and 0.980 with a misclassification rate of 23.7% and 9.8% for arteriole and venule, respectively. The proposed method outperforms the state-of-the-art methods and avoided possible error-propagation as in the segmentation-classification strategy. The proposed method holds great potential for the diagnostics and screening of various eye diseases and systemic diseases.\u3c/p\u3

Feasibility of ex vivo margin assessment with hyperspectral imaging during breast-conserving surgery: From imaging tissue slices to imaging lumpectomy specimen

Developing algorithms for analyzing hyperspectral images as an intraoperative tool for margin assessment during breast-conserving surgery requires a dataset with reliable histopatho-logic labels. The feasibility of using tissue slices hyperspectral dataset with a high correlation with histopathology for developing an algorithm for analyzing the images from the surface of lumpec-tomy specimens was investigated. We presented a method to acquire hyperspectral images from the lumpectomy surface with a high correlation with histopathology. The tissue slices dataset was compared with the dataset obtained on lumpectomy specimen and the wavelengths with a penetration depth up to the minimum sample thickness of the tissue slices were used to develop a tissue classification algorithm. Spectral differences were observed between tissue slices and lumpectomy datasets due to differences in the sample thickness between both datasets; wavelengths with a high penetration depth were able to penetrate through the thinner tissue slices, affecting the captured signal. By using only wavelengths with a penetration depth up to the minimum sample thickness of the tissue slices, the adipose tissue could be discriminated from other tissue types, but differentiating malignant from connective tissue was more challenging

Discriminating healthy from tumor tissue in breast lumpectomy specimens using deep learning-based hyperspectral imaging

Achieving an adequate resection margin during breast-conserving surgery remains challenging due to the lack of intraoperative feedback. Here, we evaluated the use of hyperspectral imaging to discriminate healthy tissue from tumor tissue in lumpectomy specimens. We first used a dataset obtained on tissue slices to develop and evaluate three convolutional neural networks. Second, we fine-tuned the networks with lumpectomy data to predict the tissue percentages of the lumpectomy resection surface. A MCC of 0.92 was achieved on the tissue slices and an RMSE of 9% on the lumpectomy resection surface. This shows the potential of hyperspectral imaging to classify the resection margins of lumpectomy specimens