解剖学的肺切除における新しいシミュレーションシステム、RPMの開発

京都大学新制・課程博士博士(医学)甲第24477号医博第4919号新制||医||1062(附属図書館)京都大学大学院医学研究科医学専攻(主査)教授 中本 裕士, 教授 波多野 悦朗, 教授 万代 昌紀学位規則第4条第1項該当Doctor of Medical ScienceKyoto UniversityDFA

Reconstructing 3d lung shape from a single 2d image during the deaeration deformation process using model-based data augmentation

Three-dimensional (3D) shape reconstruction is particularly important for computer assisted medical systems, especially in the case of lung surgeries, where large deaeration deformation occurs. Recently, 3D reconstruction methods based on machine learning techniques have achieved considerable success in computer vision. However, it is difficult to apply these approaches to the medical field, because the collection of a massive amount of clinic data for training is impractical. To solve this problem, this paper proposes a novel 3D shape reconstruction method that adopts both data augmentation techniques and convolutional neural networks. In the proposed method, a deformable statistical model of the 3D lungs is designed to augment various training data. As the experimental results demonstrate, even with a small database, the proposed method can realize 3D shape reconstruction for lungs during a deaeration deformation process from only one captured 2D image. Moreover, the proposed data augmentation technique can also be used in other fields where the training data are insufficient

Deformation analysis of surface and bronchial structures in intraoperative pneumothorax using deformable mesh registration

The positions of nodules can change because of intraoperative lung deflation, and the modeling of pneumothorax-associated deformation remains a challenging issue for intraoperative tumor localization. In this study, we introduce spatial and geometric analysis methods for inflated/deflated lungs and discuss heterogeneity in pneumothorax-associated lung deformation. Contrast-enhanced CT images simulating intraoperative conditions were acquired from live Beagle dogs. The images contain the overall shape of the lungs, including all lobes and internal bronchial structures, and were analyzed to provide a statistical deformation model that could be used as prior knowledge to predict pneumothorax. To address the difficulties of mapping pneumothorax CT images with topological changes and CT intensity shifts, we designed deformable mesh registration techniques for mixed data structures including the lobe surfaces and the bronchial centerlines. Three global-to-local registration steps were performed under the constraint that the deformation was spatially continuous and smooth, while matching visible bronchial tree structures as much as possible. The developed framework achieved stable registration with a Hausdorff distance of less than 1 mm and a target registration error of less than 5 mm, and visualized deformation fields that demonstrate per-lobe contractions and rotations with high variability between subjects. The deformation analysis results show that the strain of lung parenchyma was 35% higher than that of bronchi, and that deformation in the deflated lung is heterogeneous

Context-dependent substroke model for HMM-based on-line handwriting recognition

Describes context-dependent substroke hidden Markov models (HMMs)for on-line handwritten recognition of cursive Kanji and Hiragana characters. In order to tackle this problem, we have proposed the substroke HMM approach where a modeling unit "substroke" that is much smaller than a whole character is employed and each character is modeled as a concatenation of only 25 kinds of substroke HMMs. One of the drawbacks of this approach is that the recognition accuracy deteriorates in the case of scribbled characters, and characters where the shape of the substrokes varies a lot. We show that the context-dependent substroke modeling which depends on how the substroke connects to the adjacent substrokes is effective for achieving robust recognition of low quality characters, The successive state splitting algorithm which was mainly developed for speech recognition is employed to construct the context dependent substroke HMMs. Experimental results show that the correct recognition rate improved from 88% to 92% for cursive Kanji handwriting and from 90% to 98% for Hiragana handwriting

Patient-reported dyspnea and health predict waitlist mortality in patients waiting for lung transplantation in Japan

Background: Waitlist mortality due to donor shortage for lung transplantation is a serious problem worldwide. Currently, the selection of recipients in Japan is mainly based on the registration order. Hence, scientific evidence for risk stratification regarding waitlist mortality is urgently needed. We hypothesized that patient-reported dyspnea and health would predict mortality in patients waitlisted for lung transplantation. Methods: We analyzed factors related to waitlist mortality using data of 203 patients who were registered as candidates for lung transplantation from deceased donors. Dyspnea was evaluated using the modified Medical Research Council (mMRC) dyspnea scale, and the health status was determined with St. George's Respiratory Questionnaire (SGRQ). Results: Among 197 patients who met the inclusion criteria, the main underlying disease was interstitial lung disease (99 patients). During the median follow-up period of 572 days, 72 patients died and 96 received lung transplantation (69 from deceased donors). Univariable competing risk analyses revealed that both mMRC dyspnea and SGRQ Total score were significantly associated with waitlist mortality (p = 0.003 and p < 0.001, respectively) as well as age, interstitial lung disease, arterial partial pressure of carbon dioxide, and forced vital capacity. Multivariable competing risk analyses revealed that the mMRC and SGRQ score were associated with waitlist mortality in addition to age and interstitial lung disease. Conclusions: Both mMRC dyspnea and SGRQ score were significantly associated with waitlist mortality, in addition to other clinical variables such as patients' background, underlying disease, and pulmonary function. Patient-reported dyspnea and health may be measured through multi-dimensional analysis (including subjective perceptions) and for risk stratification regarding waitlist mortality

Resection Process Map: A novel dynamic simulation system for pulmonary resection

Read at the 99th Annual Meeting of The American Association for Thoracic Surgery, Toronto, Ontario, Canada, May 4-7, 2019.Objective: Use of 3-dimensional computed tomography for preoperative and intraoperative simulation has been introduced in the field of thoracic surgery. However, 3-dimensional computed tomography provides only static simulation, which is a significant limitation of surgical simulation. Dynamic simulation, reflecting the intraoperative deformation of the lung, has not been developed. The aim of this study was to develop a novel simulation system that generates dynamic images based on patient-specific computed tomography data. Methods: We developed an original software, the Resection Process Map, for anatomic pulmonary resection. The Resection Process Map semi-automatically generates virtual dynamic images based on patient-specific computed tomography data. We retrospectively evaluated its accuracy in 18 representative cases by comparing the virtual dynamic images with the actual surgical images. Results: In this study, 9 patients who underwent lobectomy and 9 patients who underwent segmentectomy were included. For each case, a virtual dynamic image was successfully generated semi-automatically by the Resection Process Map. The Resection Process Map accurately delineated 98.6% of vessel branches and all the bronchi. The median time required to obtain the images was 121.3 seconds. Conclusions: We successfully developed a novel dynamic simulation system, the Resection Process Map, for anatomic pulmonary resection

Preoperative detection of pleural adhesions by respiratory dynamic computed tomography

Abstract Background Video-assisted thoracic surgery (VATS) plays an important role in thoracic surgery because it is less invasive. However, the existence of severe pleural adhesions may make VATS difficult and complicated. The aim of this study was to assess the utility of inspiration and expiration computed tomography (respiratory dynamic CT (RD-CT)) in evaluation of pleural adhesions preoperatively. Methods RD-CT was performed on 107 patients undergoing thoracotomies (both VATS and open). We assessed synchronous motion during respiration on RD-CT. Comparing the results of RD-CT and intraoperative findings, we assessed the utility of preoperative evaluation. Results A negative correlation between sliding score and adhesion grade was revealed. Sliding score in adhesion negative patients was significantly higher than that in adhesion positive patients (P < 0.0001). The sensitivity of RD-CT was 63.6%, specificity was 74.1%, and accuracy was 72%. Among 62 patients with a CT-Respiration Ratio of less than 0.65, the sensitivity of RD-CT was 77.8%, specificity was 86.8%, and accuracy was 85.5%. Conclusions RD-CT may be clinically useful for detecting the presence of pleural adhesions. It can be adopted as one of the criteria for deciding the surgical approach