Lecithin-cholesterol acyltransferase (LCAT) is a liver enzyme necessary for the formation of cholesteryl esters in plasma from free cholesterol. The rare autosomal recessive disease resulting from familial deficiency of this enzyme can lead to nephropathy with kidney involvement generally being the most common cause of death. In addition, the disease process can engender corneal opacity, very low high-density lipoprotein, normochromic anemia, and nephropathy. We present this case of a 35-year-old male who initially visited for a second opinion for renal failure and nephrotic range proteinuria. He underwent renal biopsy which displayed focal segmental glomerulosclerosis-type injury pattern and was started on futile high-dose steroid therapy. A second renal biopsy coincided with the development of corneal opacity leading to a confirmatory testing of LCAT deficiency through biochemistry panel.

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In most scenarios, conditional image generation can be thought of as an inversion of the image understanding process. Since generic image understanding involves solving multiple tasks, it is natural to aim at generating images via multi-conditioning. However, multi-conditional image generation is a very challenging problem due to the heterogeneity and the sparsity of the (in practice) available conditioning labels. In this work, we propose a novel neural architecture to address the problem of heterogeneity and sparsity of the spatially multi-conditional labels. Our choice of spatial conditioning, such as by semantics and depth, is driven by the promise it holds for better control of the image generation process. The proposed method uses a transformer-like architecture operating pixel-wise, which receives the available labels as input tokens to merge them in a learned homogeneous space of labels. The merged labels are then used for image generation via conditional generative adversarial training. In this process, the sparsity of the labels is handled by simply dropping the input tokens corresponding to the missing labels at the desired locations, thanks to the proposed pixel-wise operating architecture. Our experiments on three benchmark datasets demonstrate the clear superiority of our method over the state-of-the-art and compared baselines. The source code will be made publicly available.