A Fully Automatic Approach for the Accurate Localization of the Pupils

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Autoimmunity in thymic epithelial tumors: a not yet clarified pathologic paradigm associated with several unmet clinical needs

Thymic epithelial tumors (TETs) are rare mediastinal cancers originating from the thymus, classified in two main histotypes: thymoma and thymic carcinoma (TC). TETs affect a primary lymphoid organ playing a critical role in keeping T-cell homeostasis and ensuring an adequate immunological tolerance against “self”. In particular, thymomas and not TC are frequently associated with autoimmune diseases (ADs), with Myasthenia Gravis being the most common AD present in 30% of patients with thymoma. This comorbidity, in addition to negatively affecting the quality and duration of patients’ life, reduces the spectrum of the available therapeutic options. Indeed, the presence of autoimmunity represents an exclusion criteria for the administration of the newest immunotherapeutic treatments with checkpoint inhibitors. The pathophysiological correlation between TETs and autoimmunity remains a mystery. Several studies have demonstrated the presence of a residual and active thymopoiesis in adult patients affected by thymomas, especially in mixed and lymphocytic-rich thymomas, currently known as type AB and B thymomas. The aim of this review is to provide the state of art in regard to the histological features of the different TET histotype, to the role of the different immune cells infiltrating tumor microenvironments and their impact in the break of central immunologic thymic tolerance in thymomas. We discuss here both cellular and molecular immunologic mechanisms inducing the onset of autoimmunity in TETs, limiting the portfolio of therapeutic strategies against TETs and greatly impacting the prognosis of associated autoimmune diseases

Unsupervised eye pupil localization through differential geometry and local self-similarity matching.

The automatic detection and tracking of human eyes and, in particular, the precise localization of their centers (pupils), is a widely debated topic in the international scientific community. In fact, the extracted information can be effectively used in a large number of applications ranging from advanced interfaces to biometrics and including also the estimation of the gaze direction, the control of human attention and the early screening of neurological pathologies. Independently of the application domain, the detection and tracking of the eye centers are, currently, performed mainly using invasive devices. Cheaper and more versatile systems have been only recently introduced: they make use of image processing techniques working on periocular patches which can be specifically acquired or preliminarily cropped from facial images. In the latter cases the involved algorithms must work even in cases of non-ideal acquiring conditions (e.g in presence of noise, low spatial resolution, non-uniform lighting conditions, etc.) and without user's awareness (thus with possible variations of the eye in scale, rotation and/or translation). Getting satisfying results in pupils' localization in such a challenging operating conditions is still an open scientific topic in Computer Vision. Actually, the most performing solutions in the literature are, unfortunately, based on supervised machine learning algorithms which require initial sessions to set the working parameters and to train the embedded learning models of the eye: this way, experienced operators have to work on the system each time it is moved from an operational context to another. It follows that the use of unsupervised approaches is more and more desirable but, unfortunately, their performances are not still satisfactory and more investigations are required. To this end, this paper proposes a new unsupervised approach to automatically detect the center of the eye: its algorithmic core is a representation of the eye's shape that is obtained through a differential analysis of image intensities and the subsequent combination with the local variability of the appearance represented by self-similarity coefficients. The experimental evidence of the effectiveness of the method was demonstrated on challenging databases containing facial images. Moreover, its capabilities to accurately detect the centers of the eyes were also favourably compared with those of the leading state-of-the-art methods

Comparison with state-of-the-art methods in the literature on the BioID database.

<p>Comparison with state-of-the-art methods in the literature on the BioID database.</p

Accuracy on a subset of the Extended Yale Face Database B.

<p>Accuracy on a subset of the Extended Yale Face Database B.</p

Some images of the BioID database in which the approach failed in the detection of the pupils.

<p>Reprinted from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102829#pone.0102829-BioID1" target="_blank">[27]</a> under a CC BY license, with permission from Ho B. Chang, original copyright 2001.</p

Results obtained on the BioID database and their comparison with those obtained using the strategy proposed in [20] and in [7].

<p>Results obtained on the BioID database and their comparison with those obtained using the strategy proposed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102829#pone.0102829-Leo1" target="_blank">[20]</a> and in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102829#pone.0102829-Valenti1" target="_blank">[7]</a>.</p

Some images of the Extended YALE database B in which the approach failed in the detection of the center of one or both eyes.

<p>Reprinted from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102829#pone.0102829-Georghiades1" target="_blank">[29]</a> under a CC BY license, with permission from Athinodoros S. Georghiades, original copyright 2001.</p

The scheme of the pyramidal analysis of the image intensity variations.

<p>The scheme of the pyramidal analysis of the image intensity variations.</p