논문 리뷰/의료영상28 Zhang et al., 2019, An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets # 세줄 요약 # Cancer has been one of the most threatening diseases, so our major goal is to identify malignant from benign lesions at radiology or CT imaging in the early stages, But it is difficult to collect such a large volume of images with pathological information. This paper explores two CNN models by focusing extensively on the expansion of training samples from two small pathologically prove.. 2021. 9. 27. Medina et al., 2020. Deep learning method for segmentation of rotator cuff muscles on MR images # 세줄 요약 # To develop and validate a deep convolutional neural network (CNN) method capable of (1) selecting a specific shoulder sagittal MR image (Y-view) and (2) automatically segmenting rotator cuff (RC) muscles on a Y-view. For model A, we manually selected shoulder sagittal T1 Y-view from 258 cases as ground truth to train a classification, For model B, we manually segmented subscapularis, s.. 2021. 9. 19. Kim et al., 2020, Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT # 세줄 요약 # Recent advances in fully convolutional networks have enabled automatic segmentation, however, high labeling efforts and difficulty in acquiring sufficient and high-quality training data is still a challenge. In this study, a cascaded 3D U-Net with active learning to 3 stages: first, training small dataset with manual labeling ground truth, second, training previous dataset and newly ad.. 2021. 8. 16. Shim et al., 2020, Automated rotator cuff tear classification using 3D convolutional neural network # 세줄 요약 # Rotator cuff tear (RCT) is one of the most common shoulder injuries. When diagnosing RCT, skilled orthopedists visually interpret magnetic resonance imaging (MRI) scan data. MRI data from 2,124 patients were used to train and test the VRN-based 3D CNN to classify RCT into five classes (None, Partial, Small, Medium, Large-to-Massive). The VRN-based 3D CNN outperformed orthopedists speci.. 2021. 8. 8. Kanavati et al., 2021, A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images # 세줄 요약 # The differentiation between major histological types of lung cancer, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small-cell lung cancer (SCLS) is of crucial importance for determining optimum cancer treatment. Hematoxylin and Eosin (H&E)-stained slides of small transbronchial lung biopsy (TBLB) are one of the primary sources for making a diagnosis, but if this diag.. 2021. 7. 18. Lee & Park, 2019, Application of Artificial Intelligence in Lung Cancer Sreening # 세줄요약 # 저선량 CT를 이용한 폐암검진은 그 폐암 사망률 감소 효과가 입증되었기에, 효과적인 폐암검진을 위해서는 저선량 CT에 대한 정확한 판독이 필요하다. 인공지능 기술을 활용한 저선량 CT 판독 보조시스템 개발은 영상의학 전문가들의 판독에 대한 피로도를 줄일 수 있고, 딥러닝(deep learning) 기술의 적용으로 우수한 진단성능을 보이기에 학계 및 산업계에서 큰 관심을 보이고 있다. 폐암검진에서 딥러닝 기술(인공지능 기술)을 적용할 수 있는 분야는 컴퓨터 보조 병변 검출, 판독문 생성, 폐결절의 악성도 평가, 환자의 예후 예측 등이 있다. # 상세리뷰 # 1. 저선량 CT를 이용한 폐암검진은 그 폐암 사망률 감소 효과가 입증되었기에, 효과적인 폐암검진을 위해서는 저선량 CT에 대한 정확한.. 2021. 3. 15. Han et al., 2020, Predicting Unnecessary Nodule Biopsies from a Small, Unbalanced,and Pathologically Proven Dataset by Transfer Learning # 세줄요약 # The database includes 68 biopsied nodules, 16 are pathologically proven benign and the remaining 52 are malignant. The leave-one-out and 10-folder cross validations are applied to train and test the randomly selected 68 image slices (one image slice from one nodule) in each experiment. Transfer learning from other larger datasets can supply additional information to small and unbalanced.. 2020. 10. 6. Lindsay et al., 2018, Transfer Learning Approach to Predict Biopsy-Confirmed Malignancy of Lung Nodules from Imaging Data:A Pilot Study # 세줄요약 # Dataset Includes 796 patient have pathology-confirmed diagnosis(from CT-guided biopsy) and high-resolution CT imaging data at one institution between 2012 and 2017. To avoid overfitting on small dataset, Transfer learning to train a network using open dataset(LIDC) and added three new untrained layers. These study using only 86 patients, because Lesion location was manually determined u.. 2020. 10. 5. Soongsathitanon et al., 2012, A new standard uptake values (SUV) calculation based on pixel intensity values # 세줄요약 # PET(Positron Emission Tomography) 영상은 암을 진단하고 분석하는 주로 사용되는 의료 영상으로, 이러한 PET 영상분석에 사용하는 중요한 파라미터가 SUV(Standard Uptake Value)이다. 이 논문에서는 이러한 SUV를 pixel intensity를 기반으로 프로그래밍(Matlab)하여 구하는, 기존의 'Xeleris' 같은 제품을 이용하지 않는 새로운 방법을 소개한다. SUV를 구하는 식은 다음과 같다. # 상세리뷰 # * Author: SOMPHOB SOONGSATHITANON, PAWITRA MASA-AH and MALULEE TUNTAWIROON 1. DICOM file 이 새로운 SUV를 구하는 방법은 DICOM(Digital imaging .. 2020. 7. 30. Marcus et al., 2014, Brain PET in the Diagnosis of Alzheimer’s Disease # 세줄요약 # 알츠하이머 치매를 진단하는데 Brain PET 영상이 중요한 역할을 하며, 주로 FDG-PET 영상과 Amyloid PET 영상이 영상 판독에 사용된다. FDG-PET 영상을 통해 뇌 안에서 glucose metabolism(포도당 신진대사) 분포를 보고 AD(alzheimer disease)로 인한 치매와 그 외의 다른 치매들(frontotemporal dementia & Lewy body dementia)을 구별할 수 있다. Amyloid PET 영상이 중요한 이유는 치매가 아닌 정상 환자들도 나이가 들면서 생기는 Amyloid deposition(아밀로이드 퇴적) 현상을 배제하여, 알츠하이머 병 진단에만 적절한 임상 환경(Clinical setting)을 만들어주기 때문이다. # 상.. 2020. 7. 28. 이전 1 2 3 다음
