Fully Immersive Simulation Environment (trivial phone call distraction) Video Length:
A trivial phone call scenario which distracts one resident surgeon compared to the other. This distraction resulted on higher cognitive workload and a decrease in technical accuracy during simulation surgery training.
6 July 2018, MI 03.13.010 Invited Talk by Prof. William (Sandy) Wells Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image-noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment. Estimating the uncertainty in label assignment is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning. One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications. On the other hand, most computationally efficient methods fail to estimate label uncertainty. [...]
10 September 2018, IFL Invited Talk by Dr. Hayato Itoh In colon cancer screening, polyp size estimation using only colonoscopy images or videos is difficult even for expert physicians although the size information of polyps is important for diagnosis. To construct a fully automated computer-aided diagnosis (CAD) pipeline, a robust and precise polyp size estimation method is highly desired. However, the size estimation problem of a three-dimensional object from a single two-dimensional image is ill-posed due to the lack of three-dimensional spatial information. To circumvent this challenge, we formulate a relaxed form of size estimation as a binary-size classification problem and solve it by a new deep neural network architecture. This relaxed form of size estimation is defined as a two-category classification: under and over a certain polyp dimension criterion that would provoke different clinical treatments (resecting the polyp or not). Our proposed deep learning architecture estimates the depth map from an input colonoscopic RGB image using unsupervised deep learning, and integrates RGB with the computed depth information to produce a four-channel RGB-D imagery data, that is subsequently encoded as a pipeline to extract deep RGB-D image features and facilitate the size classification into two categories: under and over 10mm polyps. We collect a large dataset of colonoscopic videos of totally over 16 hours is constructed for the evaluation of our proposed method. Using this dataset, we evaluate the accuracies of both polyp detection and binary polyp-size estimation since detection is a prerequisite step of a fully automated CAD system. The experimental results show that our proposed method achieves 79.2% accuracy for binary polyp-size classification. We also combine the extraction features by our deep learning architecture and classification of short video clips using a long short-term memory (LSTM) network. Polyp detection (if the video clip contains a polyp or not) shows 88.8% sensitivity when employing the spatio-temporal image feature extraction and classification.
7 September 2018, 01.07.014 Learning Context For Semantic Segmentation And Applications Nowadays, cameras are an integral part of many devices and systems - from mobile phones to autonomous vehicles and from medical robots to surveillance cameras. While an active field of research, the task of understanding a camera image still poses significant challenges and is not solved in general. A key in interpreting camera images correctly is not to focus on individual image areas or objects, but to use context from the whole image in order to resolve ambiguities - something that we humans are very good at...
5 July 2018, MI 01.11.018 PhD Defense by Sailesh Conjeti The task of similarity search refers to fetching an item that is closest to the query item from a reference database under the notion of some distance measure. In critical applications of large-scale search and pattern matching, exhaustive comparison is often not possible due to prohibitive computational complexity and memory overheads. Towards mitigating this, hashing has been adopted as a popular approach for performing computationally efficient approximate nearest neighbor search. [...]
27 April 2018, CAMP@TUM offers 4 International Undergraduate Excellence Awards in 2018: to the best international undergraduate applicants The Chair for Computer Aided Medical Procedures (CAMP) would like to offer 4 research awards to strongly motivated international undergraduate students interested in improving their research skills in machine learning, medical imaging, computer vision and/or augmented reality. The students are required to pursue their undergraduate studies outside Germany. The first two awards amount to 4500 Euros and the second two awards to 4000 Euros. These awards will provide financial support for the awardees during their research stay at CAMP for a period of up to three months between June 2018 and October 2019.
19 March 2018, Medvis / Karl-Heinz-Höhne Award 2018 to Markus Rempfler The Medvis / Karl-Heinz-Höhne Award 2018 has been awarded to Markus Rempfler. The Medvis Karl-Heinz Höhne Award has been established in 2004 by the 'Gesellschaft für Informatik' (GI) working group on Visual Computing in Medicine and Biology. It is awarded bi-annually to a young scientist for outstanding work on image analysis, visualization and interaction in the field of image-based diagnostics and therapy in biology and medicine.