Simulation of Stomach Specimens Generation Based on Deformation of Preoperative CT Images

2006 ◽  
pp. 178-187
Author(s):  
TrungDung Truong ◽  
Takayuki Kitasaka ◽  
Kensaku Mori ◽  
Yasuhito Suenaga
Keyword(s):  
Ct Images ◽  
Preoperative Ct

A rare case of a systemic-to-pulmonary veno-venous connection: a pause during development of pulmonary and systemic venous separation

2017 ◽  
Vol 27 (5) ◽  
pp. 1011-1013
Author(s):  
Jae Gun Kwak ◽  
Kyung-Hee Kim ◽  
Chang-Ha Lee
Keyword(s):  
Left Atrium ◽  
Pulmonary Vein ◽  
Rare Case ◽  
Ct Images ◽  
Atrial Septum ◽  
Caval Vein ◽  
Superior Caval Vein ◽  
Preoperative Ct ◽  
Diameter Orifice

AbstractA 45-year-old man with dyspnoea and palpitations exhibited a unique systemic-to-pulmonary veno-venous connection on preoperative CT images. A window of 31.5-mm diameter was evident between the superior caval vein and the middle pulmonary vein, which was normally connected to the left atrium via a 30-mm-diameter orifice. The atrial septum was intact.

Preoperative surgical simulation of laparoscopic adrenalectomy for neuroblastoma using a three-dimensional printed model based on preoperative CT images

2015 ◽  
Vol 50 (12) ◽  
pp. 2112-2115 ◽  
Author(s):  
Ryota Souzaki ◽  
Yoshiaki Kinoshita ◽  
Satoshi Ieiri ◽  
Naonori Kawakubo ◽  
Satoshi Obata ◽  
...  
Keyword(s):  
Laparoscopic Adrenalectomy ◽  
Surgical Simulation ◽  
Three Dimensional ◽  
Ct Images ◽  
Model Based ◽  
Preoperative Ct

Carcinoid Tumors of the Appendix: Are These Tumors Identifiable Prospectively on Preoperative CT?

2010 ◽  
Vol 76 (3) ◽  
pp. 273-275 ◽  
Author(s):  
Courtney A. Coursey ◽  
Rendon C. Nelson ◽  
Ricardo D. Moreno ◽  
Leslie G. Dodd ◽  
Mayur B. Patel ◽  
...  
Keyword(s):  
Carcinoid Tumor ◽  
Tumor Size ◽  
Ct Images ◽  
Database Search ◽  
Carcinoid Tumors ◽  
Current Procedural Terminology ◽  
Appendiceal Carcinoid ◽  
Preoperative Ct ◽  
Distal Location ◽  
Pathology Reports

The purpose of this study was to determine if carcinoid tumors of the appendix were identified prospectively on preoperative CT at our institution during the last decade. A surgical database search performed using the Current Procedural Terminology codes for appendectomy and colectomy yielded 2108 patients who underwent appendectomy or colectomy with removal of the appendix from January 1998 through September 2007. Pathology reports were reviewed to identify patients in whom an appendiceal carcinoid tumor was identified. Preoperative CT reports and images were reviewed. Twenty-three carcinoid tumors (1.1%; 15 women [65.2%], eight men [34.8%]; average age 54 years [range, 23 to 86 years]) were identified. Ten patients underwent preoperative CT. No tumors were identified prospectively on CT. Images were available for rereview for eight patients. Studies were acquired with 16- (n = 7) and four- (n = 1) slice CT scanners. Average reported tumor size was 6.1 mm (range, 1.5 to 15 mm; n = 18). A tip or distal location was reported for all tumors for which a location was given (n = 15). Carcinoid tumors occurred in 1.1 per cent of appendix specimens. These tumors were all less than or 1.5 cm in size. Likely as a result of their small size, none of these tumors was identified prospectively on preoperative CT.

scholarly journals Development of a deep learning model for classifying thymoma as Masaoka-Koga stage I or II via preoperative CT images

2020 ◽  
Vol 8 (6) ◽  
pp. 287-287
Author(s):  
Lei Yang ◽  
Wenjia Cai ◽  
Xiaoyu Yang ◽  
Haoshuai Zhu ◽  
Zhenguo Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Ct Images ◽  
Stage I ◽  
Preoperative Ct ◽  
Deep Learning Model

Three-dimensional liver model based on preoperative CT images as a tool to assist in surgical planning for hepatoblastoma in a child

2015 ◽  
Vol 31 (6) ◽  
pp. 593-596 ◽  
Author(s):  
Ryota Souzaki ◽  
Yoshiaki Kinoshita ◽  
Satoshi Ieiri ◽  
Makoto Hayashida ◽  
Yuhki Koga ◽  
...  
Keyword(s):  
Surgical Planning ◽  
Three Dimensional ◽  
Ct Images ◽  
Model Based ◽  
Liver Model ◽  
Preoperative Ct

scholarly journals A novel localization technique for peripheral ground glass opacity using geometric parameters measured on CT images

BMC Surgery ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mengjun Bie ◽  
Xuemin Zhao ◽  
Min Zhang ◽  
Guang Fu ◽  
Mingjian Ge
Keyword(s):  
Resection Margin ◽  
Surgical Margin ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Geometric Parameters ◽  
Ground Glass ◽  
Localization Technique ◽  
Preoperative Ct ◽  
Optimal Localization ◽  
Geometric Localization

Abstract Background Currently no optimal localization technique has been established for localization of ground glass opacity (GGO). We aimed to introduce a localization technique using geometric localization for peripheral GGO. Methods We delineated the location of pulmonary GGO using geometric method which was similar with localization of a point in a spatial coordinate system. The localization technique was based on the anatomical landmarkers (ribs or intercostal spaces, capitulum costae and sternocostal joints). The geometric parameters were measured on preoperative CT images and the targeted GGO could be identified intraoperatively according to the parameters. We retrospectively collected the data of the patients with peripheral GGOs which were localized using this method and were wedge resected between June 2019 and July 2020. The efficacy and feasibility of the localization technique were assessed. Results There were 93 patients (male 34, median = 55 years) with 108 peripheral GGOs in the study. All the targeted GGOs were successfully wedge resected in the operative field with negative surgical margin at the first attempt. For each GGO, the localization parameters could be measured in 2–4 min (median = 3 min) on CT images before operation, and surgical resection could be completed in 5–10 min (median = 7 min). A total of 106 (98.15%) GGOs achieved sufficient resection margin. No complications and deaths occurred related to the localization and surgical procedure. Conclusions The localization technique can achieve satisfactory localization success rate and good safety profile. It can provide an easy-to-use alternative to localize peripheral GGO.

scholarly journals Development and Validation of a Deep Learning Model for Preoperative Screening of Myasthenia Gravis in Patients with Thymoma based on CT Images

2020 ◽  
Author(s):  
Ying Zhu ◽  
Zhen-guo Liu ◽  
Lei Yang ◽  
Kefeng Wang ◽  
Ming-Hui Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Myasthenia Gravis ◽  
Model Building ◽  
Perioperative Complications ◽  
External Validation ◽  
Screening Method ◽  
Ct Images ◽  
Image Features ◽  
Preoperative Ct ◽  
Area Under Roc Curve

Abstract Objectives: Thymoma-associated myasthenia gravis (TAMG) is the most common paraneoplastic syndromeof thymoma. The screening of TAMG before thymoma resection is required to avoid severe perioperative complications, especiallyrespiratory failure. Herein, we developed a 3D DenseNet deep learning (DL) model based on preoperative computed tomography (CT) to detect TAMGin thymoma patients.Methods:A large cohort of 230 thymoma patientswere enrolled. 182 thymoma patients (81 with TAMG, 101 without TAMG) were used for training and model building. 48 cases from another hospital were used for external validation. A 3D-DenseNet-DL model and five machine learning models with radiomics features were performed to detectTAMG in thymoma patients. A comprehensive analysis by integrating 3D-DenseNet-DL model and general CT image features,named 3D-DenseNet-DL-based multi-model, was also performed to establish a more effective prediction model.Results: By elaborately comparing the prediction efficacy,the 3D-DenseNet-DL effectively identified TAMG patients, with a mean area under ROC curve (AUC), accuracy, sensitivity and specificity of 0.734, 0.724, 0.787 and 0.672, respectively. The effectiveness of the 3D-DenseNet-DL-based multi-model was further improved as evidenced bythe following metrics: AUC 0.766, accuracy 0.790, sensitivity 0.739 and specificity 0.801. External verification results confirmed the feasibility of this DL-based multi-model with metrics: AUC 0.730, accuracy 0.732, sensitivity 0.700 and specificity 0.690,respectively.Conclusions: Our 3D-DenseNet-DL model can effectively detect TAMG in patients with thymoma based on preoperative CT images. This model may serve as a non-invasive screening method or as a supplement to the conventional diagnostic criteria for identifyingTAMG.Key points:Thymoma-associated myasthenia gravis (TAMG) is a common paraneoplastic syndrome.3D-DenseNet-DL model can effectively detect TAMG based on preoperative CT images.This model may serve as a supplement for identifying TAMG.

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