Unilateral Congenital Lenticular Pigmentation.

Introduction: Release of pigments in the anterior chamber is frequently observed in pigment dispersion syndrome, an autosomal dominant disorder marked by bilateral pigment deposition on the anterior and possibly posterior lens capsule, zonules of the lens, trabecular meshwork, and corneal endothelium, in addition to radial, spoke-like transillumination defects in the mid peripheral iris [J Ayub Med Coll Abbottabad. 2017;29(3):412-414 and Optom Vis Sci. 1995;72(10):756-762]. Pigmentation of the anterior lens surface has also been associated with intraocular inflammation, pseudoexfoliation syndrome, siderosis, antipsychotic medication usage, and remnants of the tunica vasculosa lentis [Br J Ophthalmol. 1998;82(11):1344]. Case Presentation: A 23-year-old female presented to our eye clinic with chief complaint of mild blurring of vision in the right eye and inquired about refractive surgery. The patient denied any previous history of ocular inflammation, trauma, surgery, or use of topical or systemic medications. Slit-lamp examination of the right eye anterior segment was within normal limits except for the crystalline lens anterior capsular which showed confluent pigment deposits stellate in shape over the pupillary axis, whereas left eye examination was completely within normal limits. Ophthalmic examination of the posterior segment was normal in both eyes. Based on her previous ophthalmic history and slit-lamp examination of the right eye, a diagnosis of unilateral congenital lenticular pigmentation was made. Conclusion: Congenital lenticular pigmentation is a rare benign entity carrying no surgical indications with a relatively good visual response to optical correction. Recognition of this rare benign condition would add to the ophthalmologist's differential of ocular pigmentation and avoid unnecessary concern and follow-up in more potentially progressive disorders such as pigmentary glaucoma.

Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network.

Ultrasound (US) imaging is a mature technology that has widespread applications especially in the healthcare sector. Despite its widespread use and popularity, it has an inherent disadvantage that ultrasound images are prone to speckle and other kinds of noise. The image quality in the low-cost ultrasound imaging systems is degraded due to the presence of such noise and low resolution of such ultrasound systems. Herein, we propose a method for image enhancement where, the overall quality of the US images is improved by simultaneous enhancement of US image resolution and noise suppression. To avoid over-smoothing and preserving structural/texture information, we devise texture compensation in our proposed method to retain the useful anatomical features. Moreover, we also utilize US image formation physics knowledge to generate augmentation datasets which can improve the training of our proposed method. Our experimental results showcase the performance of the proposed network as well as the effectiveness of the utilization of US physics knowledge to generate augmentation datasets.