X-ray is one of the prevalent image modalities for the detection and
diagnosis of the human body. X-ray provides an actual anatomical structure of
an organ present with disease or absence of disease. Segmentation of disease in
chest X-ray images is essential for the diagnosis and treatment. In this paper,
a framework for the segmentation of X-ray images using artificial intelligence
techniques has been discussed. Here data has been pre-processed and cleaned
followed by segmentation using SegNet and Residual Net approaches to X-ray
images. Finally, segmentation has been evaluated using well known metrics like
Loss, Dice Coefficient, Jaccard Coefficient, Precision, Recall, Binary
Accuracy, and Validation Accuracy. The experimental results reveal that the
proposed approach performs better in all respect of well-known parameters with
16 batch size and 50 epochs. The value of validation accuracy, precision, and
recall of SegNet and Residual Unet models are 0.9815, 0.9699, 0.9574, and
0.9901, 0.9864, 0.9750 respectively.

研究探讨了使用人工智能技术对 X 射线图像进行分割的框架，将数据进行预处理和清洗后，利用 SegNet 和 Residual Net 方法对 X 射线图像进行分割，实验结果表明，所提出的方法在所有已知的参数方面表现更好

利用人工智能方法进行疾病检测、治疗和规划的医学图像分析

Medical Image Analysis for Detection, Treatment and Planning of Disease  using Artificial Intelligence Approaches

We present a novel and practical deep fully convolutional neural network
architecture for semantic pixel-wise segmentation termed SegNet. This core
trainable segmentation engine consists of an encoder network, a corresponding
decoder network followed by a pixel-wise classification layer. The architecture
of the encoder network is topologically identical to the 13 convolutional
layers in the VGG16 network. The role of the decoder network is to map the low
resolution encoder feature maps to full input resolution feature maps for
pixel-wise classification. The novelty of SegNet lies is in the manner in which
the decoder upsamples its lower resolution input feature map(s). Specifically,
the decoder uses pooling indices computed in the max-pooling step of the
corresponding encoder to perform non-linear upsampling. This eliminates the
need for learning to upsample. The upsampled maps are sparse and are then
convolved with trainable filters to produce dense feature maps. We compare our
proposed architecture with the widely adopted FCN and also with the well known
DeepLab-LargeFOV, DeconvNet architectures. This comparison reveals the memory
versus accuracy trade-off involved in achieving good segmentation performance.
SegNet was primarily motivated by scene understanding applications. Hence, it
is designed to be efficient both in terms of memory and computational time
during inference. It is also significantly smaller in the number of trainable
parameters than other competing architectures. We also performed a controlled
benchmark of SegNet and other architectures on both road scenes and SUN RGB-D
indoor scene segmentation tasks. We show that SegNet provides good performance
with competitive inference time and more efficient inference memory-wise as
compared to other architectures. We also provide a Caffe implementation of
SegNet and a web demo at this http URL

本文介绍了一种用于语义像素分割的深度全卷积神经网络，称为 SegNet。该网络架构由编码器网络、相应的解码器网络和像素分类层组成，编码器网络的拓扑结构与 VGG16 网络的 13 个卷积层完全相同，解码器使用池化索引计算非线性上采样，使得解码器可以将低分辨率的特征映射映射到全分辨率的特征映射，从而在像素级别进行分类。评估结果表明，SegNet 相对于其他现有的架构具有更好的推理性能和更高的效率。

SegNet：一种用于图像分割的深度卷积编码器 - 解码器架构

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image  Segmentation

We propose a novel deep architecture, SegNet, for semantic pixel wise image
labelling. SegNet has several attractive properties; (i) it only requires
forward evaluation of a fully learnt function to obtain smooth label
predictions, (ii) with increasing depth, a larger context is considered for
pixel labelling which improves accuracy, and (iii) it is easy to visualise the
effect of feature activation(s) in the pixel label space at any depth. SegNet
is composed of a stack of encoders followed by a corresponding decoder stack
which feeds into a soft-max classification layer. The decoders help map low
resolution feature maps at the output of the encoder stack to full input image
size feature maps. This addresses an important drawback of recent deep learning
approaches which have adopted networks designed for object categorization for
pixel wise labelling. These methods lack a mechanism to map deep layer feature
maps to input dimensions. They resort to ad hoc methods to upsample features,
e.g. by replication. This results in noisy predictions and also restricts the
number of pooling layers in order to avoid too much upsampling and thus reduces
spatial context. SegNet overcomes these problems by learning to map encoder
outputs to image pixel labels. We test the performance of SegNet on outdoor RGB
scenes from CamVid, KITTI and indoor scenes from the NYU dataset. Our results
show that SegNet achieves state-of-the-art performance even without use of
additional cues such as depth, video frames or post-processing with CRF models.

提出 SegNet，一种用于语义像素级图像标注的深度编码器 - 解码器结构。该网络通过编码器捕捉图像特征，并使用解码器映射到原始图像尺寸，实现了准确的像素级预测。在无需额外辅助信息的情况下，在几个公共基准数据集上的表现均优于最先进的方法。