Titlebook: Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2017; 20th International C Maxime Descoteaux,Lena Maier-Hein,Simon Duch

geometrician

0302-9743 tions; interventional imaging and navigation; and medical image computing. Part III: feature .extraction and classification techniques; and machine learning in medical image computing.978-3-319-66184-1978-3-319-66185-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

Amorous

Cell Lineage Tracing in Lens-Free Microscopy Videoser progression and its treatment. While recent progress in lens-free microscopy (LFM) has rendered it an inexpensive tool for continuous monitoring of these experiments, there is only little work on analysing such time-lapse sequences..We propose (1) a cell detector for LFM images based on residual

ineffectual

Substance

摻和

Cell Encoding for Histopathology Image?Classifications is time consuming and expensive. Meanwhile, with the development of cell detection and segmentation techniques, it is possible to classify pathology images by using cell-level information, which is crucial to grade different diseases; however, it is still very challenging to efficiently conduct ce

AUGUR

粗糙濫制

microscopic

Two-Stream Bidirectional Long Short-Term Memory for Mitosis Event Detection and Stage Localization iin time-lapse phase contrast microscopy image sequences. Our method consists of two steps. First, we extract candidate mitosis image sequences. Then, we solve the problem of mitosis event detection and stage localization jointly by the proposed TS-BLSTM, which utilizes both appearance and motion inf

木質

scoliosis

Semi-supervised Segmentation of Optic Cup in Retinal Fundus Images Using Variational Autoencoderlaucoma assessment. The ill-defined boundaries of optic cup makes the segmentation a lot more challenging compared to optic disc. Existing approaches have mainly used fully supervised learning that requires many labeled samples to build a robust segmentation framework. In this paper, we propose a no