Weakly supervised segmentation from extreme points

Annotation of medical images has been a major bottleneck for the development of accurate and robust machine learning models. Annotation is costly and time-consuming and typically requires expert knowledge, especially in the medical domain. Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images. We use extreme points in each dimension of a 3D medical image to constrain an initial segmentation based on the random walker algorithm. This segmentation is then used as a weak supervisory signal to train a fully convolutional network that can segment the organ of interest based on the provided user clicks. We show that the network's predictions can be refined through several iterations of training and prediction using the same weakly annotated data. Ultimately, our method has the potential to speed up the generation process of new training datasets for the development of new machine learning and deep learning-based models for, but not exclusively, medical image analysis.Comment: Accepted at the MICCAI Workshop for Large-scale Annotation of Biomedical data and Expert Label Synthesis, Shenzen, China, 201

Kidney and tumor segmentation using combined Deep learning method

This paper presents our method for automatic segmentation for kidney and tumor as part of the Kidney Tumor Segmentation Challenge (KiTS19). The KiTS19 Challenge had released a dataset of 300 unique kidney cancer patients, with manual annotations done by Climb 4 Kidney Cancer (C4KC). Here we have proposed our new combined cascade deep learning (DL) approach for solving the tasks of the challenge. We used deep learning based detection for localising kidney with the tumor, followed by deep learning based segmentation to create the labels for kidney and tumor locally. Our approach resulted in high recall (96.13) and high Jacquard score (95.4) on the randomly selected 30 volumes that were picked as the validation set

Diseño e implementación de una interfaz grafica de usuario en lenguaje python para el entrenamiento interactivo de redes profundas de segmentación de imagen médica

[ES] El objetivo de este proyecto es el desarrollo de una aplicación de escritorio para la segmentación interactiva de imágenes RMN cerebral usando técnicas de Deep Learning. En clasificación supervisada, uno de los cuellos de botella es la generación de datos etiquetados de calidad. Este proyecto está orientado a agilizar el proceso de segmentación manual de cualquier tipo de imagen médica, de forma semiautomática mediante las redes neuronales, teniendo en mente siempre al usuario, ya que esta herramienta está pensada para usuarios tanto del campo de la informática como de la medicina. Con esta herramienta se han obtenido resultados correctos a la hora de crear las segmentaciones, pero algo costosos con respecto al tiempo de creación.[EN] This project aims to develop a desktop application for interactive segmentation of RMI cerebral images using Deep Learning techniques. In labelled classification, a bottle neck is the data labelled qualified creation. This project has been oriented towards speed up the process of any medical image segmentation in a semiautomatic way by deep learning, always keep in mind the user, then this tool is oriented to users of informatic and medicine field. With this tool, correct results have been obtained when creating segmentations, but somewhat costly with respect to creation time.[CA] L’objectiu d’aquest projecte es el desenvolupament d’una aplicació d’escriptori per a la segmentació interactiva d’imatges RMN cerebral usant tècniques de Deep Learning. En classificació supervisada, un dels colls de botella es la generació de dades etiquetats de qualitat. Aquest projecte està orientat per agilitzar i a ajudar en el procés de segmentar manualment qualsevol tipus d’imatge mèdica, de forma semiautomàtica mitjançant les xarxes neuronals, tenint sempre en compte al usuari, ja que aquesta ferramenta ha sigut pensada per usuaris tant del camp de la informàtica com de la medicina. Amb aquesta aplicació s’han obtingut resultats correctes a l’hora de crear les segmentacions, però quelcom costosos respecte a al temps de creacióEstevan Moreno, M. (2019). Diseño e implementación de una interfaz grafica de usuario en lenguaje python para el entrenamiento interactivo de redes profundas de segmentación de imagen médica. http://hdl.handle.net/10251/125188TFG