Objective To investigate the early diagnosis and effective treatment of Fournier syndrome. Methods The clinical data of 385 patients with perianal abscess in this hospital between 2006 and 2009 were retrospectively analyzed for screening the patients with complication of Fournier syndrome. Results Fournier syndrome was detected in 6 patients （1.56%）， who were all cured by treating with early incision and drainage, complete debridement, effective antibiotics, and supporting therapy. Conclusions Perianal abscess can induce Fournier syndrome of perineal, genital, and abdominal wall regions, which spreads rapidly and progressively, so early diagnosis and extensive surgical debridement play a decisive role on the prognosis.
Liver cancer is a common type of malignant tumor in digestive system. At present, computed tomography (CT) plays an important role in the diagnosis and treatment of liver cancer. Segmentation of tumor lesions based on CT is thus critical in clinical diagnosis and treatment. Due to the limitations of manual segmentation, such as inefficiency and subjectivity, the automatic and accurate segmentation based on advanced computational techniques is becoming more and more popular. In this review, we summarize the research progress of automatic segmentation of liver cancer lesions based on CT scans. By comparing and analyzing the results of experiments, this review evaluate various methods objectively, so that researchers in related fields can better understand the current research progress of liver cancer segmentation based on CT scans.
The dramatically increasing high-resolution medical images provide a great deal of useful information for cancer diagnosis, and play an essential role in assisting radiologists by offering more objective decisions. In order to utilize the information accurately and efficiently, researchers are focusing on computer-aided diagnosis (CAD) in cancer imaging. In recent years, deep learning as a state-of-the-art machine learning technique has contributed to a great progress in this field. This review covers the reports about deep learning based CAD systems in cancer imaging. We found that deep learning has outperformed conventional machine learning techniques in both tumor segmentation and classification, and that the technique may bring about a breakthrough in CAD of cancer with great prospect in the future clinical practice.