Fournier's gangrene originating from the perianal region or the urogenital area: does it make a difference?

OBJECTIVE: Fournier's gangrene (FG) is a rare and serious disorder which is associated with high mortality. In the literature, there is no study evaluating clinician-, patient- and disease-related factors affecting disease outcomes according to aetiological variation in FG. In our study, laboratory results and Uludag Fournier's Gangrene Severity Index (UFGSI) score, clinical characteristics and mortality rates were compared between FG originating from perianal or from urogenital regions. METHOD: Platelet-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio, Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) and UFGSI risk scores were calculated in patients with FG at presentation to the emergency department. The patients were assigned to two groups according to FG aetiology. RESULTS: It was observed that the number of debridement interventions and the need for colostomy were significantly greater in the perianal FG group, while the need for flap or reconstruction was significantly (p=0.002) higher in the genitourinary FG group. No significant difference was detected in mortality between groups and the difference in aetiology had no significant effect on the results of the neutrophil-to-lymphocyte ratio, LRINEC or UFGSI scores. CONCLUSION: Laboratory results and UFGSI score were helpful in assessing disease severity independently from aetiology. The higher number of debridement interventions to protect anal function in the perianal group and the greater need for reconstructive surgery in the urogenital group were identified as factors that prolonged length of hospital stay.

Self-fusion for OCT noise reduction.

Reducing speckle noise is an important task for improving visual and automated assessment of retinal OCT images. Traditional image/signal processing methods only offer moderate speckle reduction; deep learning methods can be more effective but require substantial training data, which may not be readily available. We present a novel self-fusion method that offers effective speckle reduction comparable to deep learning methods, but without any external training data. We present qualitative and quantitative results in a variety of datasets from fovea and optic nerve head regions, with varying SNR values for input images.

Retinal OCT Denoising with Pseudo-Multimodal Fusion Network.

Optical coherence tomography (OCT) is a prevalent imaging technique for retina. However, it is affected by multiplicative speckle noise that can degrade the visibility of essential anatomical structures, including blood vessels and tissue layers. Although averaging repeated B-scan frames can significantly improve the signal-to-noise-ratio (SNR), this requires longer acquisition time, which can introduce motion artifacts and cause discomfort to patients. In this study, we propose a learning-based method that exploits information from the single-frame noisy B-scan and a pseudo-modality that is created with the aid of the self-fusion method. The pseudo-modality provides good SNR for layers that are barely perceptible in the noisy B-scan but can over-smooth fine features such as small vessels. By using a fusion network, desired features from each modality can be combined, and the weight of their contribution is adjustable. Evaluated by intensity-based and structural metrics, the result shows that our method can effectively suppress the speckle noise and enhance the contrast between retina layers while the overall structure and small blood vessels are preserved. Compared to the single modality network, our method improves the structural similarity with low noise B-scan from 0.559 ± 0.033 to 0.576 ± 0.031.