Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.

The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed

MRI-only radiotherapy treatment planning of the brain

Advancements in imaging methods have made it possible to create synthetic computed tomography (sCT) images from magnetic resonance imaging (MRI) data. MRI-based methods enable computed tomography (CT) to be omitted from the radiotherapy (RT) workflow and transitioning into MRI-only radiotherapy planning (RTP) of the brain. Geometric distortions in magnetic resonance (MR) images and the resulting image quality of generated sCTs are a challenge for the accuracy requirements of RT compared with CT-based methods of RTP. The current dissertation evaluated the suitability of the latest MRI scanners for MRI-only RTP, and the clinical feasibility of present quality assurance methods for measuring geometric accuracy. The clinical feasibility of MRI-only brain RTP of two different sCT generation methods was also investigated. The magnetic resonance attenuation correction (MRAC) based sCT generation method was evaluated for dosimetric accuracy. Additionally, the clinical feasibility of a commercially available deep learning based sCT generation algorithm was evaluated in terms of dosimetric and patient positioning accuracy. Based on the results of the current dissertation, the geometric accuracy of stateof-the-art MRI scanners were shown to meet the requirements of MRI-only based brain RTP. The results also showed that the sCT images generated by the MRAC method are useful for performing dose calculation in the brain. The sCTs generated using a commercial method demonstrated clinical feasibility of dose calculation and patient positioning for MRI-only brain RTP.Magneettikuvauspohjainen sädehoidon suunnittelu aivojen alueella Kuvantamismenetelmien kehitys on mahdollistanut pelkästään magneettikuvauksesta (MK) saatavaan informaatioon perustuen ns. synteettisten tietokonetomografiakuvien (sTT) muodostamisen aivojen alueella. MK-pohjaisten menetelmien avulla on mahdollista luopua kokonaan tietokonetomografiasta (TT) osana sädehoidon suunnitteluketjua ja siirtyä aivojen alueella kokonaan MKpohjaiseen sädehoidon suunnitteluun. Magneettikuvissa esiintyvät geometriset vääristymät, sekä niiden pohjalta muodostettavien sTT-kuvien laatu ovat mahdollinen haaste sädehoidon tarkkuusvaatimusten kannalta verrattuna TTkuvaukseen pohjautuviin menetelmiin. Tässä väitöstutkimuksessa arvioitiin nykyisin käytössä olevien MKlaitteiden soveltuvuutta MK-pohjaiseen sädehoidon suunnitteluun, ja nykyisin käytössä olevien geometrisen tarkkuuden laadunvarmistusmenetelmien soveltuvuutta kliiniseen laadunvalvontaan sädehoidossa. MK-pohjaisen sädehoidon suunnittelun kliinistä soveltuvuutta aivojen alueelle tutkittiin kahdella eri menetelmällä. MK-pohjaiseen vaimennuskorjausmenetelmään perustuvan sTTgenerointimallin soveltuvuutta arvioitiin annoslaskennan tarkkuuden osalta. Lisäksi tutkittiin kaupallisen, syväoppimiseen pohjautuvan algoritmin tuottamien sTTkuvien soveltuvuutta kliiniseen käyttöön annoslaskennan ja potilasasettelun verifioinnin tarkkuuden osalta. Väitöstutkimuksen tulosten perusteella voitiin osoittaa, että nykyaikaiset MK-laitteet täyttävät geometrisen tarkkuuden osalta vaatimukset MK-pohjaiseen sädehoidon suunnittelukuvantamiseen pään alueella. Lisäksi tulokset osoittivat, että MK-pohjaiseen vaimennuskorjaukseen pohjautuvalla menetelmällä luodut sTTkuvat soveltuvat sädehoidon annoslaskennan toteuttamiseen aivojen alueella. Kaupallisella menetelmällä luodut sTT-kuvat voitiin todeta soveltuviksi kliiniseen käyttöön sädehoidon suunnittelussa aivojen alueella annoslaskennan ja potilasasettelun verifioinnin tarkkuuden osalta

HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation

Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet, which connects each layer to every other layer in a feed-forward fashion, has shown impressive performances in natural image classification tasks. We propose HyperDenseNet, a 3D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. Each imaging modality has a path, and dense connections occur not only between the pairs of layers within the same path, but also between those across different paths. This contrasts with the existing multi-modal CNN approaches, in which modeling several modalities relies entirely on a single joint layer (or level of abstraction) for fusion, typically either at the input or at the output of the network. Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation. We report extensive evaluations over two different and highly competitive multi-modal brain tissue segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing on 6-month infant data and the latter on adult images. HyperDenseNet yielded significant improvements over many state-of-the-art segmentation networks, ranking at the top on both benchmarks. We further provide a comprehensive experimental analysis of features re-use, which confirms the importance of hyper-dense connections in multi-modal representation learning. Our code is publicly available at https://www.github.com/josedolz/HyperDenseNet.Comment: Paper accepted at IEEE TMI in October 2018. Last version of this paper updates the reference to the IEEE TMI paper which compares the submissions to the iSEG 2017 MICCAI Challeng

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

Segmentation of pelvic structures from preoperative images for surgical planning and guidance

Prostate cancer is one of the most frequently diagnosed malignancies globally and the second leading cause of cancer-related mortality in males in the developed world. In recent decades, many techniques have been proposed for prostate cancer diagnosis and treatment. With the development of imaging technologies such as CT and MRI, image-guided procedures have become increasingly important as a means to improve clinical outcomes. Analysis of the preoperative images and construction of 3D models prior to treatment would help doctors to better localize and visualize the structures of interest, plan the procedure, diagnose disease and guide the surgery or therapy. This requires efficient and robust medical image analysis and segmentation technologies to be developed. The thesis mainly focuses on the development of segmentation techniques in pelvic MRI for image-guided robotic-assisted laparoscopic radical prostatectomy and external-beam radiation therapy. A fully automated multi-atlas framework is proposed for bony pelvis segmentation in MRI, using the guidance of MRI AE-SDM. With the guidance of the AE-SDM, a multi-atlas segmentation algorithm is used to delineate the bony pelvis in a new \ac{MRI} where there is no CT available. The proposed technique outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. With the SDM of pelvis and its segmented surface, an accurate 3D pelvimetry system is designed and implemented to measure a comprehensive set of pelvic geometric parameters for the examination of the relationship between these parameters and the difficulty of robotic-assisted laparoscopic radical prostatectomy. This system can be used in both manual and automated manner with a user-friendly interface. A fully automated and robust multi-atlas based segmentation has also been developed to delineate the prostate in diagnostic MR scans, which have large variation in both intensity and shape of prostate. Two image analysis techniques are proposed, including patch-based label fusion with local appearance-specific atlases and multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images when limited labeled atlases are available. The proposed techniques can achieve more robust and accurate segmentation results than other multi-atlas based methods. The seminal vesicles are also an interesting structure for therapy planning, particularly for external-beam radiation therapy. As existing methods fail for the very onerous task of segmenting the seminal vesicles, a multi-atlas learning framework via random decision forests with graph cuts refinement has further been proposed to solve this difficult problem. Motivated by the performance of this technique, I further extend the multi-atlas learning to segment the prostate fully automatically using multispectral (T1 and T2-weighted) MR images via hybrid \ac{RF} classifiers and a multi-image graph cuts technique. The proposed method compares favorably to the previously proposed multi-atlas based prostate segmentation. The work in this thesis covers different techniques for pelvic image segmentation in MRI. These techniques have been continually developed and refined, and their application to different specific problems shows ever more promising results.Open Acces