Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs

Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid attenuated inversion recovery (FLAIR) sequence --where lesions appear hyperintense--. However, most of the existing retrospective datasets do not consist of FLAIR sequences. Existing missing modality imputation methods separate the process of imputation, and the process of segmentation. In this paper, we propose a method to link both modality imputation and segmentation using convolutional neural networks. We show that by jointly optimizing the imputation network and the segmentation network, the method not only produces more realistic synthetic FLAIR images from T1-w images, but also improves the segmentation of WMH from T1-w images only.Comment: Conference on Medical Imaging with Deep Learning MIDL 201

DermX: an end-to-end framework for explainable automated dermatological diagnosis

Dermatological diagnosis automation is essential in addressing the high prevalence of skin diseases and critical shortage of dermatologists. Despite approaching expert-level diagnosis performance, convolutional neural network (ConvNet) adoption in clinical practice is impeded by their limited explainability, and by subjective, expensive explainability validations. We introduce DermX and DermX+, an end-to-end framework for explainable automated dermatological diagnosis. DermX is a clinically-inspired explainable dermatological diagnosis ConvNet, trained using DermXDB, a 554 image dataset annotated by eight dermatologists with diagnoses, supporting explanations, and explanation attention maps. DermX+ extends DermX with guided attention training for explanation attention maps. Both methods achieve near-expert diagnosis performance, with DermX, DermX+, and dermatologist F1 scores of 0.79, 0.79, and 0.87, respectively. We assess the explanation performance in terms of identification and localization by comparing model-selected with dermatologist-selected explanations, and gradient-weighted class-activation maps with dermatologist explanation maps, respectively. DermX obtained an identification F1 score of 0.77, while DermX+ obtained 0.79. The localization F1 score is 0.39 for DermX and 0.35 for DermX+. These results show that explainability does not necessarily come at the expense of predictive power, as our high-performance models provide expert-inspired explanations for their diagnoses without lowering their diagnosis performance

Multi-Atlas Structure Segmentation On Medical Images

La segmentación de estructuras es muy importante en aplicaciones médicas ya que proporciona un conocimiento cuantitativo del volumen, forma o posición de las estructuras en consideración, lo cual permite el análisis y entendimiento de diferentes patologías. La técnica estándar de segmentación es la proporcionada manualmente por un experto clínico. Sin embargo este proceso es computacionalmente costoso dificultando el análisis en grandes bases de datos. Recientemente, los métodos basados en Multi-atlas han sido usados para apoyar las tareas de segmentación de imágenes cerebrales. La principal ventaja de estos métodos es debido a que son capases de proporcionar información espacial a su vez que la variabilidad anatómica es capturada mediante el uso de un conjunto de atlases etiquetados. Sin embargo la precisión de la segmentación depende de la capacidad de cada atlas para propagar sus etiquetas a la imagen objetivo, así como el método empleado para combinar las estimaciones de cada atlas. Por esta razón, la selección de atlas y el método de combinación o fusión de etiquetas, son dos importantes direcciones para mejorar el desempeño de los métodos basados en Multi-atlas. En este trabajo se proponen diferentes enfoques con el fin de mejorar la selección de atlases y la combinación de etiquetas. En primer lugar se propone un método de representación de imágenes médicas basado en Kernels la cual permite mapear el espacio original en un espacio embebido de baja dimensión donde se destacan los grupos latentes en los datos y refleje las similitudes intrínsecas. En segundo lugar, se propone una medida similitud supervisada entre imágenes, esta usa medidas de similitudes locales e información supervisada para correlacionar similitudes en apariencia con el desempeño en la segmentación. Por último, el problema de combinación de etiquetas es enmarcado como un método probabilístico no local de segmentación, donde se combinan las fortalezas de los métodos basados en parches y los métodos basados en atlas probabilísticos con el objetivo de mejorar la precisión de la segmentación. Los métodos propuestos son comparados con métodos convencionales del estado del arte en tareas de segmentación. Los resultados obtenidos muestran el potencial de los enfoques propuestos para mejorar la segmentación ya que superan los métodos convencionalesAbstract : Structure segmentation is an important task in medical applications as it give a quantitative knowledge of volume, shape or location of the structures in consideration, enabling the understanding of several pathologies. Manually segmentation is considered the current gold standard to obtain accurate segmentation. However, this process is time and resource consuming becoming impractical for large database studies. Recently, Multi-atlas based methods have been used to support automate brain structure segmentations. The advantage of this methods relies on the capacity to provide spatial information while encoding anatomical variability by using a set of prelabeled atlases. However, the accuracy of segmentation depends on the capability of each atlas to propagate the labels to the target image as well as the employed methodology for fusing the label conflicts. In this sense, atlas selection and label fusion become important directions to improve the performance of muti-atlas segmentation. In the present work several approaches to enhance atlas selection and label fusion are proposed. Firstly, a kernel based representation is proposed aiming to map the original space domain in a low dimensional embedding space where latent groups in the data are highlighted, and the intrinsic similarities are better reflected. Secondly, a supervised similarity measure is proposed which take advantage of local similarities and supervised information for linking similarity appearance with the segmentation performance. Finally, label fusion is casting as non-local probabilistic atlas-based segmentation, where the strengths of patch based and atlas-based approaches are combined to improve the segmentation accuracy. The proposed approaches are compared with conventional state of the art techniques in segmentation task. Attained results show the potential of the proposed approach to improve the segmentation outperforming the conventional state of the art methodsMaestrí

Multi-domain adaptation in brain MRI through paired consistency and adversarial learning

Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain. Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to n target domains (as long as there is paired data covering all domains). Our multi-domain adaptation method utilises a consistency loss combined with adversarial learning. We provide results on white matter lesion hyperintensity segmentation from brain MRIs using the MICCAI 2017 challenge data as the source domain and two target domains. The proposed method significantly outperforms other domain adaptation baselines