Organ Segmentation in CT Images With Weak Annotations: A Preliminary Study
Rosana El-Jurdi, Caroline Petitjean, Paul Honeine, Fahed Abdallah
LITIS, Universit´ e de Rouen, Rouen, France,Universit´ e Libanaise, Hadath, Liban Resume
Medical image segmentation has unprecedented challenges, compared to natural image segmentation, mainly because of the scarcity of annotated data-sets. Within the Cancer domain, this process is of particular interest due to the fact that treatment through irradiation planning often requires accurate delineation of target tumorized areas from organs at risk. Initially, this process was manually performed by doctors and specialists within the domain. However, such a time consuming approach is often susceptible to an unaffordable level of imprecision, which may result in missed tumorized areas, or attacking a healthy tissues. Of particular interest in this domain is the ongoing 2019 SegTHOR competition, which consists in Segmenting THoracic Organs at Risk in CT images. While the fully supervised framework (i.e., pixel-level annotation) is considered in this competition, we seek to push forward the segmentation process to a new paradigm: weakly supervised segmentation, namely training with only bounding boxes that enclose the organs.
Framework
SegTHOR Dataset
The segTHOR dataset is constituted of 60 patients characterized with lung cancer and reffered for cura- tive radiotherapy. Images within the dataset cotain ground truth segmentations of 4 organs at risk: the aorta, trachea,esophagous and the heart. Within our framework, we will focus on Binary Heart segmentA- tion. Multiclass segmentation will be studied in future work.
Challenges
Soft Organs
are Hard to detect
share same gray scale intensities with respect to neighboring tissues
Suffer from class imbalance between the organs and the corresponding back ground
CT-images are
low in contrast and high in noise
are of large sizes causing difficulty in obtaining large fine grained annotated datasets
Contributions
Propose a machine learning system that learns from weak bounding box annotations
Address the problem of class imbalance through a comparative analysis on loss functions
Proposed Model
Resume of the method
The proposed method operates in three stages:
Step 1: Data Preprocessing
Step 2: From Bounding Boxes to Initial FUlly supervised label estimates Step 3: Label Estimate Refinment through Deep neural networks
Dataset Preprocessing
From Bounding Boxes to Supervised Label Estimates
Definition : an intensity graph based algorithm
Algorithm:
1. A Gaussian mixture model is estimated
2. A graph is built and optimized using minimal cut
3. New distributions of for-ground and back-ground is done
Label Finetuning Through Neural Network Training
the model structure is a typical Auto-encoder or U-net structure.
Encoder: has a VGG16 backbone with its layers initialized with pre-trained Imagenet weights. The VGG16 network has its upper fully connected layers
removed.
Decoder:
the decoder layers are typical deconvolutional layers that extract feature maps from 16 different channels.
feature maps are then resized to theimage size and concatenated to create coarse grained label segments
Upscaling is applied using billinear interpolation
skip grams that concatenate feature maps from both the Upsampling and down sampling path are used in order to enable accurate organ localization
a threshold = 0.5 is imposed on the concatenated feature maps in order to obtain a segmentation map.
Results and Analysis
SegTHOR Dataset
References
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