ABSTRACT
Objective:To investigate the application value of three-dimensional visualization technology in management of middle hepatic vein (MHV) processing in associating liver partition and portal vein ligation for staged hepatectomy(ALPPS).Methods:The retrospective and descriptive study was conducted. The clinical data of 40 patients with right massive liver cancer or multiple right liver lesions who underwent ALPPS in the First Affiliated Hospital of Guangxi Medical University from November 2017 to August 2019 were collected. There were 34 males and 6 females, aged (44±9)years, with a range from 26 to 64 years. All patients underwent multi-slice computed tomography (CT) plain and enhanced scan of superior abdominal region before operation, and the data were transmitted to the liver visualization analysis software IQQA system with 1.5 mm thin-layer images to complete the three-dimensional reconstruction of the liver and its blood vessels. Patients were performed ALPPS based on results of three-dimensional reconstruction and intraoperative findings. Observation indicators: (1) results of preoperative three-dimensional reconstruction; (2) surgical situations; (3) follow-up. Follow-up was conducted using outpatient examinations and telephone interview to detect postopeartive survival of patients up to March 2020. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were described as M (range). Count data were represented as absolute numbers. Results:(1) Results of preoperative three-dimensional reconstruction: 40 patients underwent three-dimensional reconstruction successfully, of which 37 clearly showed MHV, tumor location and relationship between them, 3 patients showed unclearly MHV and were classified based on two-dimensional images. Of the 40 patients, 12 had MHV classified as type A, 13 as type B, 9 as type C, and 6 as type D. Three-dimensional reconstruction of vessels showed 22 with umbilical veins and 9 with anterior veins. Of the 40 patients, 35 were predicted to preserve MHV, and 5 were predicted to resect MHV. Total estimated liver volume, tumor volume, and reserved liver volume were (1 012±119)cm 3, 600 cm 3(8-2 055 cm 3), (346±80)cm 3. The ratio of future liver remnant to standard liver volume was 34%±8%. (2) Surgical situations : 40 patients underwent the first-stage ALPPS, including 35 with preservation of MHV and 5 with resection of MHV, which was accorded with preoperative prediction. Thirty-four patients underwent the second-stage ALPPS, and 6 patients had failure to receive the second-stage ALPPS due to undificiency future liver remnant. The operation time and volume of intraoperative blood loss for 40 patients undergoing first-stage ALPPS were (350±79)minutes and 300 mL(range, 100-2 600 mL). Three patients received blood transfusion and no perioperative death occurred. There were 24 patients with grade A heptic insufficiency according to criteria of International StudyGroup of Liver Surgery (ISGLS) and 16 patients with grade B heptic insufficiency after the first-stage ALPPS. Twenty-eight patients had grade Ⅰ complications of Clavien-Dindo classification, including 17 with a small pleural effusion, 10 with a small pleural and abdominal effusion, 1 with hypoproteinemia; 8 patients had grade Ⅱ complications of Clavien-Dindo classification, including 5 with pneumonia, 1 with pneumonia combined with pleural and abdominal effusion, 1 with coagulation disorders, 1 with biliary fistula; 3 patients had grade Ⅲ complications of Clavien-Dindo classification, including 2 with pneumothorax and pneumonia, 1 with pneumothorax, pneumonia and coagulation disorders; 1 patient had grade Ⅳ complications of Clavien-Dindo classification as systemic inflammatory response syndrome. All patients with complications were improved after symptomatic treatment, anti infection, transfusion of fresh frozen plasma or drainage. For the 34 patients undergoing the second-stage ALPPS, the operation time and volume of intraoperative blood loss were (320±83)minutes and 500 mL(range, 200-6 000 mL). Twelve patients received blood transfusion. There were 12 patients with grade A heptic insufficiency according to criteria of ISGLS and 22 with grade B heptic insufficiency after the second-stage ALPPS. Eighteen patients had grade Ⅰ complications of Clavien-Dindo classification, including 11 with a small pleural effusion, 7 with a small pleural and abdominal effusion; 12 patients had grade Ⅱ complications of Clavien-Dindo classification, including 4 with pneumonia, 4 with coagulation disorders, 3 with massive abdominal effusion, 1 with biliary fistula; 3 patients had grade Ⅲ complications of Clavien-Dindo classification, including 1 with pneumothorax and pneumonia, 1 with massive pleural effusion, 1 with obstructive jaundice; 1 patient had grade Ⅳ complications of Clavien-Dindo classification as pneumonia and anemia. All patients with complications were improved after symptomatic treatment, anti infection, transfusion of fresh frozen plasma or drainage. (3) Follow-up: 40 patients were followed up for 2-35 months, with a median follow-up time of 17 months. The 6-month, 1-, and 2-year survival cases were 35, 26, 21 cases. Conclusion:Three-dimensional visualization technology can clearly show the MHV classification and its relationship with tumor location, which has an important guiding significance in the decision-making of MHV management in ALPPS.
ABSTRACT
Convolutional neural networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2. Scene categories are often defined by multi-level information, including local objects, global layout, and background environment, thus leading to large intra-class variations. In addition, with the increasing number of scene categories, label ambiguity has become another crucial issue in large-scale classification. This paper focuses on large-scale scene recognition and makes two major contributions to tackle these issues. First, we propose a multi-resolution CNN architecture that captures visual content and structure at multiple levels. The multi-resolution CNNs are composed of coarse resolution CNNs and fine resolution CNNs, which are complementary to each other. Second, we design two knowledge guided disambiguation techniques to deal with the problem of label ambiguity: 1) we exploit the knowledge from the confusion matrix computed on validation data to merge ambiguous classes into a super category and 2) we utilize the knowledge of extra networks to produce a soft label for each image. Then, the super categories or soft labels are employed to guide CNN training on the Places2. We conduct extensive experiments on three large-scale image datasets (ImageNet, Places, and Places2), demonstrating the effectiveness of our approach. Furthermore, our method takes part in two major scene recognition challenges, and achieves the second place at the Places2 challenge in ILSVRC 2015, and the first place at the LSUN challenge in CVPR 2016. Finally, we directly test the learned representations on other scene benchmarks, and obtain the new state-of-the-art results on the MIT Indoor67 (86.7%) and SUN397 (72.0%). We release the code and models at https://github.com/wanglimin/MRCNN-Scene-Recognition.
ABSTRACT
Convolutional neural networks (CNNs) have recently achieved remarkable successes in various image classification and understanding tasks. The deep features obtained at the top fully connected layer of the CNN (FC-features) exhibit rich global semantic information and are extremely effective in image classification. On the other hand, the convolutional features in the middle layers of the CNN also contain meaningful local information, but are not fully explored for image representation. In this paper, we propose a novel locally supervised deep hybrid model (LS-DHM) that effectively enhances and explores the convolutional features for scene recognition. First, we notice that the convolutional features capture local objects and fine structures of scene images, which yield important cues for discriminating ambiguous scenes, whereas these features are significantly eliminated in the highly compressed FC representation. Second, we propose a new local convolutional supervision layer to enhance the local structure of the image by directly propagating the label information to the convolutional layers. Third, we propose an efficient Fisher convolutional vector (FCV) that successfully rescues the orderless mid-level semantic information (e.g., objects and textures) of scene image. The FCV encodes the large-sized convolutional maps into a fixed-length mid-level representation, and is demonstrated to be strongly complementary to the high-level FC-features. Finally, both the FCV and FC-features are collaboratively employed in the LS-DHM representation, which achieves outstanding performance in our experiments. It obtains 83.75% and 67.56% accuracies, respectively, on the heavily benchmarked MIT Indoor67 and SUN397 data sets, advancing the state-of-the-art substantially.
ABSTRACT
Survivin is a member of the IAP family, which has been shown to localize to various components of the mitotic apparatus. Survivin is not only an apoptosis inhibitor, but a mitotic regulator as well. Survivin is Absolutely undetectable in normal tissues, but over-expressed in all the most common human cancers. Survivin is considered as an important target in cancer treatment, and the research on its molecular antagonists has gained encouraging achievements.
