We compare axial 2D U-Nets and their 3D counterparts for pixel/voxel-based segmentation of five abdominal organs in CT scans. For each organ, two competing CNNs are trained. They are evaluated by performing five-fold cross-validation on 80 3D images. In a two-step concept, the relevant area containing the organ is first extracted by detected bounding boxes and then passed as input to the organ-specific U-Net. Furthermore, a random regression forest approach for the automatic detection of bounding boxes is summarized from our previous work. The results show that the 2D U-Net is mostly on par with the 3D U-Net or even outperforms it. Especially for the kidneys, it is significantly better suited in this study.
KEYWORDS: Motion models, Acquisition tracking and pointing, 3D modeling, Data modeling, Liver, Virtual reality, Lung, Image registration, Spirometry, Data acquisition
Virtual reality (VR) training simulators of liver needle insertion in the hepatic area of breathing virtual patients often need 4D image data acquisitions as a prerequisite. Here, first a population-based breathing virtual patient 4D atlas is built and second the requirement of a dose-relevant or expensive acquisition of a 4D CT or MRI data set for a new patient can be mitigated by warping the mean atlas motion. The breakthrough contribution of this work is the construction and reuse of population-based, learned 4D motion models.
For patient-specific voxel-based visuo-haptic rendering of CT scans of the liver area, the fully automatic segmentation of large volume structures such as skin, soft tissue, lungs and intestine (risk structures) is important. Using a machine learning based approach, several existing segmentations from 10 segmented gold-standard patients are learned by random decision forests individually and collectively. The core of this paper is feature selection and the application of the learned classifiers to a new patient data set. In a leave-some-out cross-validation, the obtained full volume segmentations are compared to the gold-standard segmentations of the untrained patients. The proposed classifiers use a multi-dimensional feature space to estimate the hidden truth, instead of relying on clinical standard threshold and connectivity based methods. The result of our efficient whole-body section classification are multi-label maps with the considered tissues. For visuo-haptic simulation, other small volume structures would have to be segmented additionally. We also take a look into these structures (liver vessels). For an experimental leave-some-out study consisting of 10 patients, the proposed method performs much more efficiently compared to state of the art methods. In two variants of leave-some-out experiments we obtain best mean DICE ratios of 0.79, 0.97, 0.63 and 0.83 for skin, soft tissue, hard bone and risk structures. Liver structures are segmented with DICE 0.93 for the liver, 0.43 for blood vessels and 0.39 for bile vessels.
A system for the fully automatic segmentation of the liver and spleen is presented. In a multi-atlas based segmentation
framework, several existing segmentations are deformed in parallel to image intensity based registrations
targeting the unseen patient. A new locally adaptive label fusion method is presented as the core of this paper.
In a patch comparison approach, the transformed segmentations are compared to a weak segmentation of the
target organ in the unseen patient. The weak segmentation roughly estimates the hidden truth. Traditional
fusion approaches just rely on the deformed expert segmentations only. The result of patch comparison is a
confidence weight for a neighboring voxel-label in the atlas label images to contribute to the voxel under study.
Fusion is finally carried out in a weighted averaging scheme. The new contribution is the incorporation of locally
determined confidence features of the unseen patient into the fusion process. For a small experimental set-up
consisting of 12 patients, the proposed method performs favorable to standard classifier label fusion methods. In
leave-one-out experiments, we obtain a mean Dice ratio of 0.92 for the liver and 0.82 for the spleen.
If organs are represented by a compact and non-ambiguous mathematical function, many actually interactive diagnostics on tomographic images can be performed automated. Such a representation is constructed from the organ's surface by mapping characteristic topographic structures (landmarks) onto identical function variables. Organs scanned by tomographic images series--periacetabular region of 14 hip joints imaged by X-ray CT--may be segmented. Their surface's contour points are approximated by tensor-product-B-splines (TPBSs). In a reference TPBS surface model landmarks are denoted interactively to define a mapping `variable-pair of the TPBS vs. landmark'. The patient TPBS models are mapped onto the reference model by fitting the model function values. The fit, and so the landmark identification, is performed by a homology function, which is applied to the patient model's variable plane. For simply shaped organs, the transformation of the tomographic to the topographic representation was possible using only the values and first order derivatives of the TPBSs. The presented landmark identification method avoids unnecessary assumptions of model deformation mechanism and has low computational costs.
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