2,341 research outputs found
SFNet: Learning Object-aware Semantic Correspondence
We address the problem of semantic correspondence, that is, establishing a
dense flow field between images depicting different instances of the same
object or scene category. We propose to use images annotated with binary
foreground masks and subjected to synthetic geometric deformations to train a
convolutional neural network (CNN) for this task. Using these masks as part of
the supervisory signal offers a good compromise between semantic flow methods,
where the amount of training data is limited by the cost of manually selecting
point correspondences, and semantic alignment ones, where the regression of a
single global geometric transformation between images may be sensitive to
image-specific details such as background clutter. We propose a new CNN
architecture, dubbed SFNet, which implements this idea. It leverages a new and
differentiable version of the argmax function for end-to-end training, with a
loss that combines mask and flow consistency with smoothness terms.
Experimental results demonstrate the effectiveness of our approach, which
significantly outperforms the state of the art on standard benchmarks.Comment: cvpr 2019 oral pape
Label-driven weakly-supervised learning for multimodal deformable image registration
Spatially aligning medical images from different modalities remains a
challenging task, especially for intraoperative applications that require fast
and robust algorithms. We propose a weakly-supervised, label-driven formulation
for learning 3D voxel correspondence from higher-level label correspondence,
thereby bypassing classical intensity-based image similarity measures. During
training, a convolutional neural network is optimised by outputting a dense
displacement field (DDF) that warps a set of available anatomical labels from
the moving image to match their corresponding counterparts in the fixed image.
These label pairs, including solid organs, ducts, vessels, point landmarks and
other ad hoc structures, are only required at training time and can be
spatially aligned by minimising a cross-entropy function of the warped moving
label and the fixed label. During inference, the trained network takes a new
image pair to predict an optimal DDF, resulting in a fully-automatic,
label-free, real-time and deformable registration. For interventional
applications where large global transformation prevails, we also propose a
neural network architecture to jointly optimise the global- and local
displacements. Experiment results are presented based on cross-validating
registrations of 111 pairs of T2-weighted magnetic resonance images and 3D
transrectal ultrasound images from prostate cancer patients with a total of
over 4000 anatomical labels, yielding a median target registration error of 4.2
mm on landmark centroids and a median Dice of 0.88 on prostate glands.Comment: Accepted to ISBI 201
Alakzatok lineáris deformációinak becslése és orvosi alkalmazásai = Estimation of Linear Shape Deformations and its Medical Applications
A projekt fĹ‘ eredmĂ©nye egy általánosan használhatĂł, teljesen automatikus alakzat regisztráciĂłs mĂłdszer, amely az alábbi tulajdonságokkal rendelkezik: • nincs szĂĽksĂ©g pontmegfeleltetĂ©sekre illetve iteratĂv optimalizálĂł algoritmusokra; • kĂ©pes 2D lineáris Ă©s (invertálhatĂł) projektĂv deformáciĂłk, valamint 3D affin deformáciĂłk meghatározására; • robusztus a geometriai Ă©s szegmentálási hibákra; • lineáris idĹ‘komplexitásĂş, ami lehetĹ‘vĂ© teszi nagy felbontásĂş kĂ©pek közel valĂłs idejű illesztĂ©sĂ©t. Publikusan elĂ©rhetĹ‘vĂ© tettĂĽnk 3 demĂł programot, amelyek a 2D Ă©s 3D affin, valamint sĂkhomográfia regisztráciĂłs algoritmusainkat implementálják. Továbbá kifejlesztettĂĽnk egy prototĂpus szoftvert csĂpĹ‘protĂ©zis röntgenkĂ©pek illesztĂ©sĂ©re, amit átadtunk a projektben közreműködĹ‘ radiolĂłgusoknak további felhasználásra. Az eredmĂ©nyeinket a terĂĽlet vezetĹ‘ konferenciáin ( pl. ICCV, ECCV) illetve vezetĹ‘ folyĂłiratokban (pl. IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition). A projekten dolgozĂł egyik MSc hallgatĂł második helyezĂ©st Ă©rt el az OTDK-n. Domokos Csaba PhD fokozatot szerzett, továbbá munkáját Kuba Attila dĂjjal ismerte el a KĂ©pfeldolgozĂłk Ă©s AlakfelismerĹ‘k Társasága. A projekt eredmĂ©nyeirĹ‘l rĂ©szletesebb informáciĂł a projekt honlapokon találhatĂł: • http://www.inf.u-szeged.hu/ipcg/projects/AFFSHAPE.html • http://www.inf.u-szeged.hu/ipcg/projects/AffinePuzzle.html • http://www.inf.u-szeged.hu/ipcg/projects/diffeoshape.html | The main achievement of the project is a fully functional automatic shape registration method with the following properties: • it doesn’t need established point correspondences nor the use of iterative optimization algorithms; • capable of recovering 2D linear and (invertible) projective shape deformations as well as affine distortions of 3D shapes; • robust in the presence of geometric noise and segmentation errors; • has a linear time complexity allowing near real-time registration of high resolution images. 3 demo programs are publicly available implementing our affine 2D, 3D and planar homography registration algorithms. Furthermore, we have developed a prototype software for aligning hip prosthesis X-ray images, which has been transfered to collaborating radiologists for further exploitation. Our results have been presented at top conferences (e.g. ICCV, ECCV) and in leading journals (e.g. IEEE Trans. on Patt. Anal. & Mach. Intell., Patt. Rec.). An MSc student working on the project received the second price of the National Scientific Student Conference. Csaba Domokos obtained his PhD degree and his work has been awarded the Attila Kuba Prize of the Hungarian Association for Image Processing and Pattern Recognition. More details about our results can be found at: • http://www.inf.u-szeged.hu/ipcg/projects/AFFSHAPE.html • http://www.inf.u-szeged.hu/ipcg/projects/AffinePuzzle.html • http://www.inf.u-szeged.hu/ipcg/projects/diffeoshape.htm
Deformable GANs for Pose-based Human Image Generation
In this paper we address the problem of generating person images conditioned
on a given pose. Specifically, given an image of a person and a target pose, we
synthesize a new image of that person in the novel pose. In order to deal with
pixel-to-pixel misalignments caused by the pose differences, we introduce
deformable skip connections in the generator of our Generative Adversarial
Network. Moreover, a nearest-neighbour loss is proposed instead of the common
L1 and L2 losses in order to match the details of the generated image with the
target image. We test our approach using photos of persons in different poses
and we compare our method with previous work in this area showing
state-of-the-art results in two benchmarks. Our method can be applied to the
wider field of deformable object generation, provided that the pose of the
articulated object can be extracted using a keypoint detector.Comment: CVPR 2018 versio
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
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