Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques

A new approach for the segmentation of gland units in histological images is proposed with the aim of contributing to the improvement of the prostate cancer diagnosis. Clustering methods on several colour spaces are applied to each sample in order to generate a binary mask of the different tissue components. From the mask of lumen candidates, the Locally Constrained Watershed Transform (LCWT) is applied as a novel gland segmentation technique never before used in this type of images. 500 random gland candidates, both benign and pathological, are selected to evaluate the LCWT technique providing results of Dice coefficient of 0.85. Several shape and textural descriptors in combination with contextual features and a fractal analysis are applied, in a novel way, on different colour spaces achieving a total of 297 features to discern between artefacts and true glands. The most relevant features are then selected by an exhaustive statistical analysis in terms of independence between variables and dependence with the class. 3.200 artefacts, 3.195 benign glands and 3.000 pathological glands are obtained, from a data set of 1468 images at 10x magnification. A careful strategy of data partition is implemented to robustly address the classification problem between artefacts and glands. Both linear and non-linear approaches are considered using machine learning techniques based on Support Vector Machines (SVM) and feedforward neural networks achieving values of sensitivity, specificity and accuracy of 0.92, 0.97 and 0.95, respectivelyThis work has been funded by the Ministry of Economy, Industry and Competitiveness under the SICAP project (DPI2016-77869-C2-1-R). The work of Adri´an Colomer has been supported by the Spanish FPI Grant BES-2014-067889. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this researchGarcía-Pardo, JG.; Colomer, A.; Naranjo Ornedo, V.; Peñaranda, F.; Sales, MÁ. (2018). Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 642-650. https://doi.org/10.1007/978-3-030-03493-1_67S642650Gleason, D.F.: Histologic grading and clinical staging of prostatic carcinoma. In: Urologic Pathology (1977)Naik, S., Doyle, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information. In: MIAAB Workshop, pp. 1–8 (2007)Nguyen, K., Sabata, B., Jain, A.K.: Prostate cancer grading: gland segmentation and structural features. Pattern Recogn. Lett. 33(7), 951–961 (2012)Kwak, J.T., Hewitt, S.M.: Multiview boosting digital pathology analysis of prostate cancer. Comput. Methods Programs Biomed. 142, 91–99 (2017)Ren, J., Sadimin, E., Foran, D.J., Qi, X.: Computer aided analysis of prostate histopathology images to support a refined gleason grading system. In: SPIE Medical Imaging, International Society for Optics and Photonics, p. 101331V (2017)Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Berlin (2013)Nguyen, K., Sarkar, A., Jain, A.K.: Structure and context in prostatic gland segmentation and classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 115–123. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_15Beare, R.: A locally constrained watershed transform. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1063–1074 (2006)Gertych, A., et al.: Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Comput. Med. Imaging Graph. 46, 197–208 (2015)Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)Huang, P., Lee, C.: Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans. Med. Imaging 28(7), 1037–1050 (2009)Ruifrok, A.C., Johnston, D.A., et al.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291–299 (2001

Incremental cost-effectiveness of screening and laser treatment for diabetic retinopathy and macular edema in Malawi

Objective To investigate the economic impact of introducing targeted screening and laser photocoagulation treatment for sight-threatening diabetic retinopathy and macular edema in a setting with no previous screening or laser treatment for diabetic retinopathy in sub-Saharan Africa. Materials and methods A cohort Markov model was built to compare combined targeted screening and laser treatment for patients with sight-threatening diabetic retinopathy and macular edema against no intervention. Primary outcomes were incremental cost per quality-adjusted life year (QALY) gained and per disability-adjusted life year (DALY) averted. Primary data were collected on 357 participants from the Malawi Diabetic Retinopathy Study, a prospective, observational cohort study. Multiple scenarios were explored and a probabilistic sensitivity analysis was performed. Results In the base case (age: 50 years, service utilization rate: 80%), the cost of the intervention and the years of severe visual impairment averted per patient screened were $209 and 2.2 years respectively. Applying the World Health Organization threshold of cost-effectiveness for Malawi ($679), the base case was cost-effective when QALYs were used ($400 per QALY gained) but not when DALYs were used ($766 per DALY averted). The intervention was more cost-effective when it targeted younger patients (age: 30 years) and less cost-effective when the utilization rate was lowered to 50%. Conclusions Annual photographic screening of diabetic patients attending medical diabetes clinics in Malawi, with the provision of laser treatment for those with sight-threatening diabetic retinopathy and macular edema, appears to be cost-effective in terms of QALYs gained, in our base case scenario. Cost-effectiveness improves if services are utilized more intensively and extended to younger patients

t' at the LHC: the physics of discovery

A search for a fourth family at the LHC is presently a low priority, but we argue that an effective search can be conducted early with only a few inverse femtobarns of data. We discuss a method based on invariant masses of single jets for identifying the $W$'s originating from heavy quark decays. This can significantly increase signal to background in the reconstruction of the $t'$ mass. We also study the various types of physics that can impact the background estimate, most notably higher order effects, initial state radiation, and models of the underlying event.Comment: 16 pages, 12 figures, small improvements, version to appear in JHE

Superconducting ground state study of valence skip compound AgSnSe2_2

The valence-skipped superconductors are natural candidates for unconventional superconductivity, as they can exhibit a negative effective, attractive interaction for electron-pairing. This work reports comprehensive XRD, magnetization, specific heat and muon spin rotation and relaxation measurements ($\mu$SR) on a valence-skipped compound: AgSnSe$_2$. The temperature dependence of the electronic specific heat ($C_{el}(T)$) and of the upper critical field ($H_{c2}(T)$) provide evidence of two-gap superconductivity, which is also confirmed by our transverse-field $\mu$SR measurements. Our zero-field $\mu$SR measurements suggest preserved time-reversal symmetry in the superconducting ground state of AgSnSe$_2$.Comment: 8 pages, 6 figure