ADAM Challenge: Detecting Age-Related Macular Degeneration From Fundus Images.

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models.

Fusing Results of Several Deep Learning Architectures for Automatic Classification of Normal and Diabetic Macular Edema in Optical Coherence Tomography.

Diabetic Macular Edema (DME) is a severe eye disease that can lead to irreversible blindness if it is left untreated. DME diagnosis still relies on manual evaluation from opthalmologists, thus the process is time consuming and diagnosis may be subjective. This paper presents two novel DME detection frameworks: (1) combining features from three pre-trained Convolutional Neural Networks: AlexNet, VggNet and GoogleNet and performing feature space reduction using Principal Component Analysis and (2) a majority voting scheme based on a plurality rule between classifications from AlexNet, VggNet and GoogleNet. Experiments were conducted using Optical Coherence Tomography datasets retrieved from the Singapore Eye Research Institute and the Chinese University Hong Kong. The results are evaluated using a Leave-Two-Patients-Out Cross Validation at the volume level. This method improves DME classification with an accuracy of 93.75%, which is similar to the best algorithms so far on the same data sets.

Localization of optic disc and fovea in retinal images using intensity based line scanning analysis.

Accurate detection of diabetic retinopathy (DR) mainly depends on identification of retinal landmarks such as optic disc and fovea. Present methods suffer from challenges like less accuracy and high computational complexity. To address this issue, this paper presents a novel approach for fast and accurate localization of optic disc (OD) and fovea using one-dimensional scanned intensity profile analysis. The proposed method utilizes both time and frequency domain information effectively for localization of OD. The final OD center is located using signal peak-valley detection in time domain and discontinuity detection in frequency domain analysis. However, with the help of detected OD location, the fovea center is located using signal valley analysis. Experiments were conducted on MESSIDOR dataset, where OD was successfully located in 1197 out of 1200 images (99.75%) and fovea in 1196 out of 1200 images (99.66%) with an average computation time of 0.52s. The large scale evaluation has been carried out extensively on nine publicly available databases. The proposed method is highly efficient in terms of quickly and accurately localizing OD and fovea structure together compared with the other state-of-the-art methods.

Complete sternal cleft - A rare congenital malformation and its repair in a 3-month-old boy: A case report.

Complete midline sternal cleft is a rare congenital anomaly resulting from failed midline ventral fusion of the sternal bars. Very few cases of complete sternal cleft have been described in literature. We present a case of complete sternal cleft in a 3-month-old child. The patient underwent primary closure of the defect using stainless steel wires.

Retroperitoneal Mature Cystic Teratoma in a Neonate: A Case Report.

We report a case of retroperitoneal mature cystic teratoma in a 2-day-old neonate. Diagnostic and surgical procedure including its complexity and relevant literature review has been discussed.

Cloacal Exstrophy with Mature Teratoma: A Rare Association in a Neonate.

Cloacal exstrophy is a very rare and complex malformation. We report a neonate of cloacal exstrophy with mature teratoma presenting as a component of exstrophy. To our knowledge this has not been reported in the literature.

Successful Separation of Omphalopagus Twins: A Case Report.

Omphalopagus twins are conjoined twins sharing part of gastrointestinal system and abdominal wall. These types of twins have best chances of survival if successfully separated. We report a case of successfully separated omphalopagus twins at day six of life.

Autoamputation of the foot caused by untreated gas gangrene: a case report.

Gas-producing diabetic foot infections are limb-threatening emergencies commonly encountered by foot and ankle surgeons. Appropriate treatment includes aggressive surgical debridement, parenteral antibiotics, and postoperative wound care. The authors present a unique case of a patient who refused treatment for a deep-space gas-producing infection, resulting in autoamputation of the foot. The authors also discuss the confusion surrounding the definition of gas gangrene.