This paper addresses the problem of liver cancer segmentation in Whole Slide Image (WSI). We propose a multi-scale image processing method based on automatic end-to-end deep neural network algorithm for segmentation of cancer area. A seven-levels gaussian pyramid representation of the histopathological image was built to provide the texture information in different scales. In this work, several neural architectures were compared using the original image level for the training procedure. The proposed method is based on U-Net applied to seven levels of various resolutions (pyramidal subsumpling). The predictions in different levels are combined through a voting mechanism. The final segmentation result is generated at the original image level. Partial color normalization and weighted overlapping method were applied in preprocessing and prediction separately. The results show the effectiveness of the proposed multi-scales approach achieving better scores compared to the state-of-the-art.
Queen Mary University of London（伦敦玛丽女王大学）；Communication University of Zhejiang（浙江传媒学院）；University of Leicester（莱斯特大学）
Cancer segmentation in whole-slide images is a fundamental step for viable tumour burden estimation, which is of great value for cancer assessment. However, factors like vague boundaries or small regions dissociated from viable tumour areas make it a challenging task. Considering the usefulness of multi-scale features in various vision-related tasks, we present a structure-aware scale-adaptive feature selection method for efficient and accurate cancer segmentation. Based on a segmentation network with a popular encoder-decoder architecture, a scale-adaptive module is proposed for selecting mor…
University of Jordan（约旦大学）；King Abdullah II School for IT（阿卜杜拉二世信息技术学院）
Effective ultrasound tissue characterization is usually hindered by complex tissue structures. The interlacing of speckle patterns complicates the correct estimation of backscatter distribution parameters. Nakagami parametric imaging based on localized shape parameter mapping can model different backscattering conditions. However, performance of the constructed Nakagami image depends on the sensitivity of the estimation method to the backscattered statistics and scale of analysis. Using a fixed focal region of interest in estimating the Nakagami parametric image would increase estimation varia…
Recent breakthroughs in object detection and image classification using Convolutional Neural Networks (CNNs) are revolutionizing the state of the art in medical imaging, and microscopy in particular presents abundant opportunities for computer vision algorithms to assist medical professionals in diagnosis of diseases ranging from malaria to cancer. High resolution scans of microscopy slides called Whole Slide Images (WSIs) offer enough information for a cancer pathologist to come to a conclusion regarding cancer presence, subtype, and severity based on measurements of features within the slide…
Correct treatment of urothelial carcinoma patients is dependent on accurate grading and staging of the cancer tumour. This is determined manually by a pathologist by examining the histological whole-slide images (WSI). The large size of these images makes this a time-consuming and challenging task. The WSI contain a variety of tissue types, and a method for defining diagnostic relevant regions would have several advantages for visualization as well as further input to automated diagnosis systems. We propose an automatic multiscale method for classification of tiles from WSI of urothelial carci…
School of Engineering, Griffith University（格里菲斯大学工程学院）；School of Computer Science and Technology, Wuhan University of Technology（武汉理工大学计算机科学与技术学院）；Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences（中国科学院东北地理与农业生态研究所）
Leaf image recognition techniques have been actively researched for plant species identification. However it remains unclear whether leaf patterns can provide sufficient information for cultivar recognition. This paper reports the first attempt on soybean cultivar recognition from plant leaves which is not only a challenging research problem but also important for soybean cultivar evaluation, selection and production in agriculture. In this paper, we propose a novel multiscale sliding chord matching (MSCM) approach to extract leaf patterns that are distinctive for soybean cultivar identificati…
Histopathology tissue analysis is considered the gold standard in cancer diagnosis and prognosis. Given the large size of these images and the increase in the number of potential cancer cases, an automated solution as an aid to histopathologists is highly desirable. In the recent past, deep learning-based techniques have provided state of the art results in a wide variety of image analysis tasks, including analysis of digitized slides. However, the size of images and variability in histopathology tasks makes it a challenge to develop an integrated framework for histopathology image analysis. W…
Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images
Visvesvaraya National Institute of Technology（维斯瓦拉亚国家理工学院）；AIIMS Nagpur（那格浦尔全印度医学科学研究所）；Indian Institute of Technology Mandi（曼迪印度理工学院）；Universitat de Barcelona（巴塞罗那大学）
Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual H&E staining
University of California, Davis（加利福尼亚大学戴维斯分校）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Emory University School of Medicine（埃默里大学医学院）；UC Davis Health（加州大学戴维斯分校医疗系统）
A Hyperspectral Imaging Dataset and Methodology for Intraoperative Pixel-Wise Classification of Metastatic Colon Cancer in the Liver
Ruđer Bošković Institute（鲁杰尔·博什科维奇研究所）；Technical University Munich（慕尼黑工业大学）；University of Montreal（蒙特利尔大学）；Photon etc.（Photon etc.公司）；Clinical Hospital Center Zagreb（萨格勒布临床医院中心）；University of Zagreb（萨格勒布大学）；Clinical Hospital Dubrava（杜布拉瓦临床医院）
University of California, Los Angeles（加州大学洛杉矶分校）；Stanford University（斯坦福大学）
Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors. We introduce Deep Active Lesion Segmentation (DALS), a fully automated segmentation framework for that leverages the powerful nonlinear feature extraction abilities of fully Convolutional Neural Networks (CNNs) and the precise boundary delineation abilities of Active Contour Models (ACMs). Our DALS framework benefits from an improved level-set ACM formulation with a per-pixel-parameterized energy fun…
Department of Computer Science, Vanderbilt University（范德堡大学计算机科学系）
Histopathology has played an essential role in cancer diagnosis. With the rapid advances in convolutional neural networks (CNN). Various CNN-based automated pathological image segmentation approaches have been developed in computer-assisted pathological image analysis. In the past few years, Transformer neural networks (Transformer) have shown the unique merit of capturing the global long-distance dependencies across the entire image as a new deep learning paradigm. Such merit is appealing for exploring spatially heterogeneous pathological images. However, there have been very few, if any, stu…
While artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, the question of how best to incorporate these algorithms into clinical workflows remains relatively unexplored. We investigated how AI can affect pathologist performance on the task of differentiating between two subtypes of primary liver cancer, hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC). We developed an AI diagnostic assistant using a deep learning model and evaluated its effect on the diagnostic performance of eleven pathologists with varying levels of expertise.…
Fourier ptychographic microscopy (FPM) is a computational approach geared towards creating high-resolution and large field-of-view images without mechanical scanning. To acquire color images of histology slides, it often requires sequential acquisitions with red, green, and blue illuminations. The color reconstructions often suffer from coherent artifacts that are not presented in regular incoherent microscopy images. As a result, it remains a challenge to employ FPM for digital pathology applications, where resolution and color accuracy are of critical importance. Here we report a deep learni…
