Fetal brain tissue annotation and segmentation challenge results.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.

Prediction of pathological staging and grading of renal clear cell carcinoma based on deep learning algorithms.

OBJECTIVE: Deep learning algorithms were used to develop a model for predicting the staging and grading of renal clear cell carcinoma to inform clinicians' treatment plans. METHODS: Clinical and pathological information was collected from 878 patients diagnosed with renal clear cell carcinoma in the Department of Urology, Peking University First Hospital. The patients were randomly assigned to the test set (n = 702) or the verification set (n = 176). Pathological staging and grading of renal clear cell carcinoma were predicted by preoperative clinical variables using deep learning algorithms. Receiver operating characteristic curves were used to evaluate the predictive accuracy as measured by the area under the receiver operating characteristic curve (AUC). RESULTS: For tumor pathological staging, AUC values of 0.933, 0.947, and 0.948 were obtained using the BiLSTM, CNN-BiLSTM, and CNN-BiGRU models, respectively. For tumor pathological grading, the AUC values were 0.754, 0.720, and 0.770, respectively. CONCLUSIONS: The proposed model for predicting renal clear cell carcinoma allows for accurate projection of the staging and grading of renal clear cell carcinoma and helps clinicians optimize individual treatment plans.

Carotid cavernous fistulas treated by endovascular covered stent grafts with follow-up results.

Historically, carotid cavernous fistulas (CCFs) are usually treated with detachable balloons and coils. While coronary covered stent grafts have been sporadically used in the intracranial vasculature, only a few such cases have been reported in the literature. We present our experience of seven patients with eight CCFs, treated by the placement of covered stents, and provide their clinical and angiographic follow-up results. From 28 cases of CCF treated at our department between November 2000 and January 2007, a total of eight post-traumatic CCF were treated between May 2005 and January 2007 by positioning a Jostent coronary stent graft (Abbott Vascular, Redwood City, CA) in the intracranial ICA. These patients had periodic follow-up examinations (at 12-30 months) with 5 of them receiving a follow-up angiographic examination (at 6-9 months). Immediate post-procedural angiograms demonstrated total exclusion in five CCFs, residual filling in three CCFs, and preserved ICA patency in all CCFs. Symptoms related to the CCF regressed within 1-15 days after the covered stent placement. There was no mortality and no immediate post-procedural morbidity related to the CCF covered stent treatment; however, one patient died of acute myocardial infarction in 10 days following treatment. Six patients showed complete clinical recovery during their clinical follow-up. The angiographic follow-up (mean, 8 months) in five patients with six CCFs revealed regressed residual filling, stable occlusion of all CCFs, and an ICA patency (one case of asymptomatic ICA occlusion) rate of 83.3%. Covered stents are a promising therapeutic alternative for patients with CCF in whom fistulas cannot be successfully occluded with detachable balloons or coils. Our results and the reported results showed that a covered stent could occlude the fistula and preserve the ICA without hemodynamic changes or negative clinical effects. Covered stent grafts could soon become a first-line therapy as experience with this device increases, materials continue to improve, and more data are accumulated.