Detection-aided liver lesion segmentation using deep learning

A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the according treatments. In this work we propose a method to segment the liver and its lesions from Computed Tomography (CT) scans using Convolutional Neural Networks (CNNs), that have proven good results in a variety of computer vision tasks, including medical imaging. The network that segments the lesions consists of a cascaded architecture, which first focuses on the region of the liver in order to segment the lesions on it. Moreover, we train a detector to localize the lesions, and mask the results of the segmentation network with the positive detections. The segmentation architecture is based on DRIU, a Fully Convolutional Network (FCN) with side outputs that work on feature maps of different resolutions, to finally benefit from the multi-scale information learned by different stages of the network. The main contribution of this work is the use of a detector to localize the lesions, which we show to be beneficial to remove false positives triggered by the segmentation network. Source code and models are available at https://imatge-upc.github.io/liverseg-2017-nipsws/ .Comment: NIPS 2017 Workshop on Machine Learning for Health (ML4H

Segmentation of pathological liver tissue with Dilated Fully Convolutional Networks: A Preliminary Study

Early detection of liver cancer, whether from primary occurrence or from metastization is highly important to establish informed treatment decisions. Accurate delineation of the liver tissues of interest facilitates quantitative assessment of the regions of interest, treatment application, and prognosis. Segmentation of the liver in Computer Tomography (CT) images allows the extraction of the three-dimensional (3D) structure of the liver tissues in which the observation of their relative position to one another is particularly important in treatment scenarios of radiation therapy or interventional surgery planning. The adequate receptive field for the segmentation of such a big organ in CT images, from the remaining neighbouring organs was very successfully improved by the use of the state-of-the-art Convolutional Neural Networks (CNN) algorithms, however, certain issues still arise and are highly dependent of pre-or post-processing methods to refine the final segmentations. Here, the effects of Dilated Convolutional Networks is proposed, for the purpose of improving segmentation of liver tissues in CT. The introduction of a dilation module allowed the concatenation of feature maps with a richer contextual information. The hierarchical learning process given by different dilated convolutional layers is analysed quantitatively. Experiments on the MICCAI Lits challenge dataset are described achieving segmentations with a mean Dice coefficients of 95.57% and 59.36% for the liver and liver tumour, using a total number 30 CT test volumes. (c) ENBENG 2019. All Rights Reserved

Deep segmentation of the liver and the hepatic tumors from abdomen tomography images

A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two output-classified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation

Detection-aided medical image segmentation using deep learning

The details of the work will be defined once the student reaches the destination institution.A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and also to assess the response to the according treatments. In this thesis we propose a method to segment the liver and its lesions from Computed Tomography (CT) scans, as well as other anatomical structures and organs of the human body. We have used Convolutional Neural Networks (CNNs), that have proven good results in a variety of tasks, including medical imaging. The network to segment the lesions consists of a cascaded architecture, which first focuses on the liver region in order to segment the lesion. Moreover, we train a detector to localize the lesions and just keep those pixels from the output of the segmentation network where a lesion is detected. The segmentation architecture is based on DRIU (Maninis, 2016), a Fully Convolutional Network (FCN) with side outputs that work at feature maps of different resolutions, to finally benefit from the multi-scale information learned by different stages of the network. Our pipeline is 2.5D, as the input of the network is a stack of consecutive slices of the CT scans. We also study different methods to benefit from the liver segmentation in order to delineate the lesion. The main focus of this work is to use the detector to localize the lesions, as we demonstrate that it helps to remove false positives triggered by the segmentation network. The benefits of using a detector on top of the segmentation is that the detector acquires a more global insight of the healthiness of a liver tissue compared to the segmentation network, whose final output is pixel-wise and is not forced to take a global decision over a whole liver patch. We show experiments with the LiTS dataset for the lesion and liver segmentation. In order to prove the generality of the segmentation network, we also segment several anatomical structures from the Visceral dataset