Journal: Diagnostics

Article Title: Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network

doi: 10.3390/diagnostics12092161

Figure Lengend Snippet: Comparing our approach to the other WSI segmentation techniques that consider the time factor as well.

Article Snippet: Model , U-net , Panoptes , CNN.

Techniques:

Journal: Frontiers in Neurology

Article Title: Feasibility of Multimodal MRI-Based Deep Learning Prediction of High Amino Acid Uptake Regions and Survival in Patients With Glioblastoma

doi: 10.3389/fneur.2019.01305

Figure Lengend Snippet: Results of the receiver operating characteristic analysis to predict 6-month progression-free survival by imaging (MRI, PET, and MRI-learned PET from U-Net 4 ) and clinical variables.

Article Snippet: To investigate the effect of different multimodal MRI protocols on the performance of the U-Net system, three different U-Net systems (U-Net 1(Siemens) trained by multimodal data of Siemens Protocol, U-Net 2(Philips) trained by multimodal data of Philips Protocol, and U-Net 3 trained by combined multimodal [Siemens+Philips] Protocol) were separately implemented using Google TensorFlow library ( www.tensorflow.org ).

Techniques: Imaging

Journal: BMC Musculoskeletal Disorders

Article Title: Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol

doi: 10.1186/s12891-023-06153-y

Figure Lengend Snippet: Other work relevant to automated segmentation of knee structures [ 19 – 23 ]. DSC = Dice Score Coefficient, OAI = Osteoarthritis Initiative, DESS = Double Echo Steady State

Article Snippet: F. Ambellan et al. [ ] , DESS , 88 , 3D U-Net , Femoral cartilage (0.89 ± 2.41) Medial tibial cartilage (86.1 ± 5.33) Lateral tibial cartilage (90.4 ± 2.42) , OAI Imorphics dataset.

Techniques: Standard Deviation