301 research outputs found
Anatomy-Aware Lymph Node Detection in Chest CT using Implicit Station Stratification
Finding abnormal lymph nodes in radiological images is highly important for
various medical tasks such as cancer metastasis staging and radiotherapy
planning. Lymph nodes (LNs) are small glands scattered throughout the body.
They are grouped or defined to various LN stations according to their
anatomical locations. The CT imaging appearance and context of LNs in different
stations vary significantly, posing challenges for automated detection,
especially for pathological LNs. Motivated by this observation, we propose a
novel end-to-end framework to improve LN detection performance by leveraging
their station information. We design a multi-head detector and make each head
focus on differentiating the LN and non-LN structures of certain stations.
Pseudo station labels are generated by an LN station classifier as a form of
multi-task learning during training, so we do not need another explicit LN
station prediction model during inference. Our algorithm is evaluated on 82
patients with lung cancer and 91 patients with esophageal cancer. The proposed
implicit station stratification method improves the detection sensitivity of
thoracic lymph nodes from 65.1% to 71.4% and from 80.3% to 85.5% at 2 false
positives per patient on the two datasets, respectively, which significantly
outperforms various existing state-of-the-art baseline techniques such as
nnUNet, nnDetection and LENS
2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers
Enlarged lymph nodes (LNs) can provide important information for cancer
diagnosis, staging, and measuring treatment reactions, making automated
detection a highly sought goal. In this paper, we propose a new algorithm
representation of decomposing the LN detection problem into a set of 2D object
detection subtasks on sampled CT slices, largely alleviating the curse of
dimensionality issue. Our 2D detection can be effectively formulated as linear
classification on a single image feature type of Histogram of Oriented
Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We
exploit both simple pooling and sparse linear fusion schemes to aggregate these
2D detection scores for the final 3D LN detection. In this manner, detection is
more tractable and does not need to perform perfectly at instance level (as
weak hypotheses) since our aggregation process will robustly harness collective
information for LN detection. Two datasets (90 patients with 389 mediastinal
LNs and 86 patients with 595 abdominal LNs) are used for validation.
Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume
(FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10
FP/vol.), for the mediastinal and abdominal datasets respectively. Our results
compare favorably to previous state-of-the-art methods.Comment: This article will be presented at MICCAI (Medical Image Computing and
Computer-Assisted Intervention) 201
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions
Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system
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