Segmenting brain tumors accurately and reliably is an essential part of cancer diagnosis and treatment planning. Brain tumor segmentation of glioma patients is a challenging task because of the wide variety of tumor sizes, shapes, positions, scanning modalities, and scanner’s acquisition protocols. Many convolutional neural network (CNN) based methods have been proposed to solve the problem of brain tumor segmentation and achieved great success. However, most previous studies do not fully take into account multiscale tumors and often fail to segment small tumors, which may have a significant impact on finding early-stage cancers. This paper deals with the brain tumor segmentation of any sizes, but specially focuses on accurately identifying small tumors, thereby increasing the performance of the brain tumor segmentation of overall sizes. Instead of using heavyweight networks with multi-resolution or multiple kernel sizes, we propose a novel approach for better segmentation of small tumors by dilated convolution and multi-task learning. Dilated convolution is used for multiscale feature extraction, however it does not work well with very small tumor segmentation. For dealing with small-sized tumors, we try multi-task learning, where an auxiliary task of feature reconstruction is used to retain the features of small tumors. The experiment shows the effectiveness of segmenting small tumors with the proposed method. This paper contributes to the detection and segmentation of small tumors, which have seldom been considered before and the new development of hierarchical analysis using multi-task learning.

One essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT). Many recent studies have focused on several organs such as the lung, heart, esophagus, trachea, liver, aorta, kidney, and prostate. However, among the above organs, the esophagus is one of the most difficult organs to segment because of its small size, ambiguous boundary, and very low contrast in CT images. To address these challenges, we propose a fully automated framework for the esophagus segmentation from CT images. The proposed method is based on the processing of slice images from the original three-dimensional (3D) image so that our method does not require large computational resources. We employ the spatial attention mechanism with the atrous spatial pyramid pooling module to locate the esophagus effectively, which enhances the segmentation performance. To optimize our model, we use group normalization because the computation is independent of batch sizes, and its performance is stable. We also used the simultaneous truth and performance level estimation (STAPLE) algorithm to reach robust results for segmentation. Firstly, our model was trained by k-fold cross-validation. And then, the candidate labels generated by each fold were combined by using the STAPLE algorithm. And as a result, Dice and Hausdorff Distance scores have an improvement when applying this algorithm to our segmentation results. Our method was evaluated on SegTHOR and StructSeg 2019 datasets, and the experiment shows that our method outperforms the state-of-the-art methods in esophagus segmentation. Our approach shows a promising result in esophagus segmentation, which is still challenging in medical analyses.

This article introduces a framework for predicting the survival of brain tumor patients by analyzing magnetic resonance images. The prediction of brain tumor survival is challenging due to the limited size of available datasets. To overcome the issue of overfitting, we propose a self-supervised learning method that involves identifying image patches from the same or different images. By recognizing intra-and inter-image differences, the network can learn the relationships between local spatial windows in the same image and across different images. In addition to analyzing local information, we also incorporate a global structure awareness network to capture global information from the entire image. Our proposed method shows a strong correlation between local spatial relationships and survivor class prediction in FLAIR MRI brain images. We evaluate our method using the BraTS 2020 validation dataset and observe that our method outperforms others in accuracy and SpearmanR correlation metrics.

Distorted medical images can significantly hamper medical diagnosis, notably in the analysis of Computer Tomography (CT) images and organ segmentation specifics. Therefore, improving diagnostic imagery accuracy and reconstructing damaged portions are important for medical diagnosis. Recently, these issues have been studied extensively in the field of medical image inpainting. Inpainting techniques are emerging in medical image analysis since local deformations in medical modalities are common because of various factors such as metallic implants, foreign objects or specular reflections during the image captures. The completion of such missing or distorted regions is important for the enhancement of post-processing tasks such as segmentation or classification. In this paper, a novel framework for medical image inpainting is presented by using a multi-task learning model for CT images targeting the learning of the shape and structure of the organs of interest. This novelty has been accomplished through simultaneous training for the prediction of edges and organ boundaries with the image inpainting, while state-of-the-art methods still focus only on the inpainting area without considering the global structure of the target organ. Therefore, our model reproduces medical images with sharp contours and exact organ locations. Consequently, our technique generates more realistic and believable images compared to other approaches. Additionally, in quantitative evaluation, the proposed method achieved the best results in the literature so far, which include a PSNR value of 43.44 dB and SSIM of 0.9818 for the square-shaped regions; a PSNR value of 38.06 dB and SSIM of 0.9746 for the arbitrary-shaped regions. The proposed model generates the sharp and clear images for inpainting by learning the detailed structure of organs. Our method was able to show how promising the method is when applying it in medical image analysis, where the completion of missing or distorted regions is still a challenging task.

Binarization is an important step for most of document analysis systems. Regarding music score images with a complex background, the existence of background clutters with a variety of shapes and colors creates many challenges for the binarization. This paper presents a model for binarization of the complex background music score images by fusion of deep convolutional neural networks. Our model is directly trained from image regions using pixel values as inputs and the binary ground truth as labels. By utilizing the generalization capability of the residual network backbone and useful feature learning ability of dense layer, the proposed network structures can differentiate foreground pixels from background clutters, minimize the possibility of overfitting phenomenon and thus can deal with complex background noises appearing in the music score images. Comparing to traditional algorithms, binary images generated by our method have a cleaner background and better-preserved strokes. The experiments with captured and synthetic music score images show promising results compared to existing methods.