Fully-automated, CT-only GTV contouring for palliative head and neck radiotherapy.

Planning for palliative radiotherapy is performed without the advantage of MR or PET imaging in many clinics. Here, we investigated CT-only GTV delineation for palliative treatment of head and neck cancer. Two multi-institutional datasets of palliative-intent treatment plans were retrospectively acquired: a set of 102 non-contrast-enhanced CTs and a set of 96 contrast-enhanced CTs. The nnU-Net auto-segmentation network was chosen for its strength in medical image segmentation, and five approaches separately trained: (1) heuristic-cropped, non-contrast images with a single GTV channel, (2) cropping around a manually-placed point in the tumor center for non-contrast images with a single GTV channel, (3) contrast-enhanced images with a single GTV channel, (4) contrast-enhanced images with separate primary and nodal GTV channels, and (5) contrast-enhanced images along with synthetic MR images with separate primary and nodal GTV channels. Median Dice similarity coefficient ranged from 0.6 to 0.7, surface Dice from 0.30 to 0.56, and 95th Hausdorff distance from 14.7 to 19.7 mm across the five approaches. Only surface Dice exhibited statistically-significant difference across these five approaches using a two-tailed Wilcoxon Rank-Sum test (p ≤ 0.05). Our CT-only results met or exceeded published values for head and neck GTV autocontouring using multi-modality images. However, significant edits would be necessary before clinical use in palliative radiotherapy

종양의 3차원적 위치 파악을 위한 메쉬 구조의 3D 모델링 기술 개발 및 임상적 유용성 평가

학위논문(박사) -- 서울대학교대학원 : 의과대학 의학과, 2022.2. 김희찬.배 경: 3D 프린팅은 인체 종양의 3차원적 위치를 파악하기 위한 방법으로써, 이미 의학 영역에 다양한 목적으로 보급되어 사용되고 있으나, 높은 비용과 긴 제작 시간은 3D 모델의 활용에 제약이 되고 있다. 목 적: 첫 번째 연구의 목표는 인체 장기와 그 장기가 포함하고 있는 종양의 관계를 묘사하기 위해 메쉬 구조의 3D 모델링이라고 하는 새로운 기법을 개발하는 것으로, 비용의 절감 및 출력 시간에서 장점을 보일 것으로 가정하였다. 두 번째 연구는 딥러닝을 이용한 개별화된 3D 갑상선 모델을 메쉬 구조로 제작하고 수술 전 동의를 받는 과정에 이용하여 본 기술의 임상적 유용성을 평가하고자 하였다. 방 법: 메쉬 구조의 3차원 모델링은 단층 영상에서 일정 간격으로 좌표를 추출하고, 이를 그물망(메쉬) 형태로 연결하는 구조의 레플리카를 생성한다. 인접한 해부학적 구조는 메쉬의 밀도를 변화시켜 출력하는 방식으로 구분할 수 있으며, 정상 조직과 종양은 대조되는 색상으로 표시한다. 이를 이용한 임상 연구를 위해, 수술 전 동의서 작성에 3D 모형을 이용하는 전향적 무작위 배정 대조군 비교 임상시험(KCT0005069)을 설계하였다. 갑상선 수술을 받는 환자 53명을 대상으로, 수술 동의서 작성 시 개별화된 3D 프린팅 모델을 사용하는 군과, 사용하지 않고 기존의 방식대로 동의서를 작성하는 두 그룹으로 나누었다. 이 과정에서 U-Net 기반의 딥러닝 아키텍처와 메쉬 구조의 3D 모델링 기법을 활용하여 개인화된 3D 모델을 제작하였다. 결 과: 메쉬 구조의 3D 모델링을 이용해 융합 적층 모델링(FDM) 방식의 3D 프린터를 통해 출력한 결과, 낮은 비용($0.05/cm3)과 제작 시간(1.73 min/cm3)을 보였다. 실제 3D 프린팅 된 모형은 수술 중 절제된 검체와 비교했을 때 장기-종양 해부학 및 인접 조직을 시각적으로 구분하는데 충분한 수준을 보였다. 이후 시행한 전향적 임상 연구에서, 53명의 환자들의 갑상선 모형의 평균 3D 프린팅 시간은 258.9분이었고 평균 제작비는 환자 1인당 USD 4.23였다. 모든 3차원 모형은 종양과 갑상선의 크기, 위치, 해부학적 관계를 효과적으로 반영할 수 있었다. 수술 동의서 작성시 개별화된 3D 프린팅 모델을 제공받은 그룹은 4가지 범주(일반 지식, 수술의 이점, 수술의 위험, 만족도) 모두에서 통계적으로 유의한 수준의 개선을 보였다 (모두 p <0.05). 모든 환자는 수술 후 개별화된 3D 모델을 제공받았으며, 질병, 수술 및 가능한 합병증 및 전반적인 만족도 향상에 도움이 되었음을 확인할 수 있었다. 결 론: 개별화된 3D 갑상선 모델은 수술 전 동의서 작성 과정에서 환자의 이해와 만족도를 향상시키는 효과적인 도구가 될 수 있었다. 새롭게 고안한 메쉬 구조의 3D 모델링 기법은 장기의 크기/윤곽 및 종양의 위치를 시각화 하는데 효과적이었으며, 이러한 방법론은 개별화된 치료를 위한 해부학적 모델링을 용이하게 하고, 수술 동의서 작성과 같은 설명 과정에 있어, 환자의 효과적인 의학적 지식 습득을 도울 수 있음을 확인하였다.Background: As a method of three-dimensional (3D) localization of tumor, 3D printing is introduced to medicine. However, the high costs and lengthy production times required have limited their application. Objectives: The goal of the first study was to develop a new and less costly 3D modeling method, “mesh-type 3D modeling”, to depict organ–tumor relations. The second study was designed to evaluate the clinical usefulness of a personalized mesh-type 3D-printed thyroid gland model for obtaining informed consent. Methods: For the mesh-type 3D modeling, coordinates were extracted at a specified distance interval from tomographic images, connecting them to create mesh-work replicas. Adjacent constructs were depicted by density variations, showing anatomical targets (i.e., tumors) in contrasting colors. A randomized, controlled prospective clinical trial (KCT0005069) was designed. A total of 53 patients undergoing thyroid surgery were randomly assigned to two groups: with or without a 3D-printed model of their thyroid lesion upon obtaining informed consent. A U-Net-based deep learning architecture and the mesh-type 3D modeling technique were used to fabricate the personalized 3D model. Results: To establish the mesh-type 3D modeling technique, an array of organ-solid tumor models was printed via a Fused Deposition Modeling 3D printer at a lower cost ($0.05 USD/cm3) and time expenditure (1.73 min/cm3). Printed models helped promote visual appreciation of organ-tumor anatomy and adjacent tissues. In the prospective clinical study, the mean 3D printing time was 258.9 min, and the mean price for production was USD 4.23 for each patient. The size, location, and anatomical relationship of the tumor with respect to the thyroid gland could be effectively presented. The group provided with personalized 3D-printed models significantly improved across all four categories (i.e., general knowledge, benefits of surgery, risks of surgery, and satisfaction; all p < .05). All patients received a personalized 3D model after surgery and found it helpful toward understanding the disease, operation, and possible complications, as well as enhancing their overall satisfaction. Conclusion: The personalized 3D-printed thyroid gland model may be an effective tool for improving a patient’s understanding and satisfaction during the informed consent process. Furthermore, the mesh-type 3D modeling reproduced glandular size/contour and tumor location, readily approximating the surgical specimen. This newly devised mesh-type 3D printing method may facilitate anatomical modeling for personalized care and improve patient awareness during informed surgical consent.Chapter 1. Introduction 1 Chapter 2. Materials and Methods 4 Chapter 3. Results 24 Chapter 4. Discussion 43 Chapter 5. Conclusions 49 Acknowledgements 50 Bibliography 51 Abstract in Korean 55박

U-Net and its variants for medical image segmentation: theory and applications

U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. These traits provide U-net with a very high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. As the potential of U-net is still increasing, in this review we look at the various developments that have been made in the U-net architecture and provide observations on recent trends. We examine the various innovations that have been made in deep learning and discuss how these tools facilitate U-net. Furthermore, we look at image modalities and application areas where U-net has been applied.Comment: 42 pages, in IEEE Acces

Automated Analysis of Drill-Core Images Using Convolutional Neural Network

Drill cores provide geological and geotechnical information essential for mineral and hydrocarbon exploration. Modern core scanners can automatically produce a large number of high-resolution core-tray images or unwrapped-core images, which encode important rock properties, such as lithology and geological structures. Current core-image analysis methods, however, are based on outdated algorithms that lack generalization and robustness. In addition, current methods focus on using log data while core images often provide more reliable information about the subsurface formations. With the new era of technology and the evolution of big data and artificial intelligence, core images will be an important asset for subsurface characterization. The future of core analysis, driven by the digital archiving of cores, needs to be considered since the manual core description and its extensive time and labor requirements are outdated. This dissertation aims to lay the foundation of a ‘Digital Geologist’ using advanced machine learning algorithms. It develops and evaluates intelligent workflows using Convolutional Neural Networks (CNNs) to automate core-image analysis, and thus facilitate the evaluation of natural resources. It explores the feasibility of extracting different rock features from core images. First, advanced CNNs are utilized to predict major lithologies of rocks from core-tray images and an overall workflow is optimized for lithology prediction. Second, a CNN is created to assess the physical condition of cores and determine intact core sections to calculate the rock quality designation (RQD) index, which is essential in many geotechnical applications. Third, an innovative approach is developed to extract fractures from unwrapped-core images and determine fracture depth and orientation. The workflow is based on using a state-of-the-art CNN model for instance segmentation, the Mask Region-based Convolutional Neural Network (Mask R-CNN). Lastly, fracture analysis from unwrapped-core images is further studied to obtain more detailed characteristics represented by fracture apertures. Overall, the thesis proposes a transformed workflow of core-image analysis that can be a platform for future studies with potential application in the mining and petroleum industries

MEDICAL MACHINE INTELLIGENCE: DATA-EFFICIENCY AND KNOWLEDGE-AWARENESS

Traditional clinician diagnosis requires massive manual labor from experienced doctors, which is time-consuming and costly. Computer-aided systems are therefore proposed to reduce doctors’ efforts by using machines to automatically make diagnosis and treatment recommendations. The recent success in deep learning has largely advanced the field of computer-aided diagnosis by offering an avenue to deliver automated medical image analysis. Despite such progress, there remain several challenges towards medical machine intelligence, such as unsatisfactory performance regarding challenging small targets, insufficient training data, high annotation cost, the lack of domain-specific knowledge, etc. These challenges cultivate the need for developing data-efficient and knowledge-aware deep learning techniques which can generalize to different medical tasks without requiring intensive manual labeling efforts, and incorporate domain-specific knowledge in the learning process. In this thesis, we rethink the current progress of deep learning in medical image analysis, with a focus on the aforementioned challenges, and present different data-efficient and knowledge-aware deep learning approaches to address them accordingly. Firstly, we introduce coarse-to-fine mechanisms which use the prediction from the first (coarse) stage to shrink the input region for the second (fine) stage, to enhance the model performance especially for segmenting small challenging structures, such as the pancreas which occupies only a very small fraction (e.g., < 0.5%) of the entire CT volume. The method achieved the state-of-the-art result on the NIH pancreas segmentation dataset. Further extensions also demonstrated effectiveness for segmenting neoplasms such as pancreatic cysts or multiple organs. Secondly, we present a semi-supervised learning framework for medical image segmentation by leveraging both limited labeled data and abundant unlabeled data. Our learning method encourages the segmentation output to be consistent for the same input under different viewing conditions. More importantly, the outputs from different viewing directions are fused altogether to improve the quality of the target, which further enhances the overall performance. The comparison with fully-supervised methods on multi-organ segmentation confirms the effectiveness of this method. Thirdly, we discuss how to incorporate knowledge priors for multi-organ segmentation. Noticing that the abdominal organ sizes exhibit similar distributions across different cohorts, we propose to explicitly incorporate anatomical priors on abdominal organ sizes, guiding the training process with domain-specific knowledge. The approach achieves 84.97% on the MICCAI 2015 challenge “Multi-Atlas Labeling Beyond the Cranial Vault”, which significantly outperforms previous state-of-the-art even using fewer annotations. Lastly, by rethinking how radiologists interpret medical images, we identify one limitation for existing deep-learning-based works on detecting pancreatic ductal adenocarcinoma is the lack of knowledge integration from multi-phase images. Thereby, we introduce a dual-path network where different paths are connected for multi-phase information exchange, and an additional loss is added for removing view divergence. By effectively incorporating multi-phase information, the presented method shows superior performance than prior arts on this matter