| 1. |
|
|
| 2. |
- Klein, T., et al.
(författare)
-
Modeling of Multi-View 3D Freehand Radio Frequency Ultrasound
- 2012
-
Ingår i: Medical Image Computing and Computer-assisted Intervention - Miccai 2012, Pt I. - Berlin, Heidelberg : Springer. - 9783642334153 - 9783642334146 ; , s. 422-429
-
Konferensbidrag (refereegranskat)abstract
- Nowadays ultrasound (US) examinations are typically performed with conventional machines providing two dimensional imagery. However, there exist a multitude of applications where doctors could benefit from three dimensional ultrasound providing better judgment, due to the extended spatial view. 3D freehand US allows acquisition of images by means of a tracking device attached to the ultrasound transducer. Unfortunately, view dependency makes the 3D representation of ultrasound a non-trivial task. To address this we model speckle statistics, in envelope-detected radio frequency (RF) data, using a finite mixture model (FMM), assuming a parametric representation of data, in which the multiple views are treated as components of the FMM. The proposed model is show-cased with registration, using an ultrasound specific distribution based pseudo-distance, and reconstruction tasks, performed on the manifold of Gamma model parameters. Example field of application is neurology using transcranial US, as this domain requires high accuracy and data systematically features low SNR, making intensity based registration difficult. In particular, 3D US can be specifically used to improve differential diagnosis of Parkinson's disease (PD) compared to conventional approaches and is therefore of high relevance for future application.
|
|
| 3. |
- Moghaddam, Mandana Javanshir, et al.
(författare)
-
DEeP Random Walks
- 2013
-
Ingår i: Medical Imaging 2013. - : SPIE - International Society for Optical Engineering. - 9780819494436 ; , s. 86693O-
-
Konferensbidrag (refereegranskat)abstract
- In this paper, we proposed distance enforced penalized (DEeP) random walks segmentation framework to delineate coupled boundaries by modifying classical random walks formulations. We take into account curves inter-dependencies and incorporate associated distances into weight function of conventional random walker. This effectively leverages segmentation of weaker boundaries guided by stronger counterparts, which is the main advantage over classical random walks techniques where the weight function is only dependent on intensity differences between connected pixels, resulting in unfavorable outcomes in the context of poor contrasted images. First, we applied our developed algorithm on synthetic data and then on cardiac magnetic resonance (MR) images for detection of myocardium borders. We obtained encouraging results and observed that proposed algorithm prevents epicardial border to leak into right ventricle or cross back into endocardial border that often observe when conventional random walker is used. We applied our method on forty cardiac MR images and quantified the results with corresponding manual traced borders as ground truths. We found the Dice coefficients 70% +/- 14% and 43% +/- 14% respectively for DEeP random walks and conventional one.
|
|
| 4. |
- Ravikumar, Sadhana, et al.
(författare)
-
Improved Segmentation of Deep Sulci in Cortical Gray Matter Using a Deep Learning Framework Incorporating Laplace’s Equation
- 2023
-
Ingår i: Information Processing in Medical Imaging - 28th International Conference, IPMI 2023, Proceedings. - 1611-3349 .- 0302-9743. - 9783031340475 ; 13939 LNCS, s. 692-704
-
Konferensbidrag (refereegranskat)abstract
- When developing tools for automated cortical segmentation, the ability to produce topologically correct segmentations is important in order to compute geometrically valid morphometry measures. In practice, accurate cortical segmentation is challenged by image artifacts and the highly convoluted anatomy of the cortex itself. To address this, we propose a novel deep learning-based cortical segmentation method in which prior knowledge about the geometry of the cortex is incorporated into the network during the training process. We design a loss function which uses the theory of Laplace’s equation applied to the cortex to locally penalize unresolved boundaries between tightly folded sulci. Using an ex vivo MRI dataset of human medial temporal lobe specimens, we demonstrate that our approach outperforms baseline segmentation networks, both quantitatively and qualitatively.
|
|
