HENLE FIBER LAYER MAPPING WITH DIRECTIONAL OPTICAL COHERENCE TOMOGRAPHY.

PURPOSE: To perform a macular volumetric and topographic analysis of Henle fiber layer (HFL) from retinal scans acquired by directional optical coherence tomography. METHODS: Thirty healthy eyes of 17 subjects were imaged using the Heidelberg spectral-domain optical coherence tomography (Spectralis, Heidelberg Engineering, Heidelberg, Germany) with varied horizontal and vertical pupil entry. Manual segmentation of HFL was performed from retinal sections of horizontally and vertically tilted optical coherence tomography images acquired within macular 20 × 20° area. Total HFL volume, mean HFL thickness, and HFL coverage area within Early Treatment for Diabetic Retinopathy Study grid were calculated from mapped images. RESULTS: Henle fiber layer of 30 eyes were imaged, segmented and mapped. The mean total HFL volume was 0.74 ± 0.08 mm 3 with 0.16 ± 0.02 mm 3 , 0.18 ± 0.03 mm 3 , 0.17 ± 0.02 mm 3 , and 0.19 ± 0.03 mm 3 for superior, temporal, inferior, and nasal quadrants, respectively. The mean HFL thickness was 26.5 ± 2.9 µ m. Central 1-mm macular zone had the highest mean HFL thickness with 51.0 ± 7.6 µ m. The HFL coverage that have thickness equal or above to the mean value had a mean 10.771 ± 0.574 mm 2 of surface area. CONCLUSION: Henle fiber layer mapping is a promising tool for structural analysis of HFL. Identifying a normative data of HFL morphology will allow further studies to investigate HFL involvement in various ocular and systemic disorders.

FourierNet: Shape-Preserving Network for Henle's Fiber Layer Segmentation in Optical Coherence Tomography Images.

Henle's fiber layer (HFL), a retinal layer located in the outer retina between the outer nuclear and outer plexiform layers (ONL and OPL, respectively), is composed of uniformly linear photoreceptor axons and Müller cell processes. However, in the standard optical coherence tomography (OCT) imaging, this layer is usually included in the ONL since it is difficult to perceive HFL contours on OCT images. Due to its variable reflectivity under an imaging beam, delineating the HFL contours necessitates directional OCT, which requires additional imaging. This paper addresses this issue by introducing a shape-preserving network, FourierNet, which achieves HFL segmentation in standard OCT scans with the target performance obtained when directional OCT is available. FourierNet is a new cascaded network design that puts forward the idea of benefiting the shape prior of the HFL in the network training. This design proposes to represent the shape prior by extracting Fourier descriptors on the HFL contours and defining an additional regression task of learning these descriptors. FourierNet then formulates HFL segmentation as concurrent learning of regression and classification tasks, in which Fourier descriptors are estimated from an input image to encode the shape prior and used together with the input image to construct the HFL segmentation map. Our experiments on 1470 images of 30 OCT scans of healthy-looking macula reveal that quantifying the HFL shape with Fourier descriptors and concurrently learning them with the main segmentation task leads to significantly better results. These findings indicate the effectiveness of designing a shape-preserving network to facilitate HFL segmentation by reducing the need to perform directional OCT imaging.

3D printed personalized magnetic micromachines from patient blood-derived biomaterials.

While recent wireless micromachines have shown increasing potential for medical use, their potential safety risks concerning biocompatibility need to be mitigated. They are typically constructed from materials that are not intrinsically compatible with physiological environments. Here, we propose a personalized approach by using patient blood­derivable biomaterials as the main construction fabric of wireless medical micromachines to alleviate safety risks from biocompatibility. We demonstrate 3D printed multiresponsive microswimmers and microrollers made from magnetic nanocomposites of blood plasma, serum albumin protein, and platelet lysate. These micromachines respond to time-variant magnetic fields for torque-driven steerable motion and exhibit multiple cycles of pH-responsive two-way shape memory behavior for controlled cargo delivery and release applications. Their proteinaceous fabrics enable enzymatic degradability with proteinases, thereby lowering risks of long-term toxicity. The personalized micromachine fabrication strategy we conceptualize here can affect various future medical robots and devices made of autologous biomaterials to improve biocompatibility and smart functionality.