Pulmonary Lobe Segmentation With Probabilistic Segmentation of the Fissures and a Groupwise Fissure Prior

Lung vessel connectivity map as anatomical prior knowledge for deep learning-based lung lobe segmentation

Purpose

Our study investigates the potential benefits of incorporating prior anatomical knowledge into a deep learning (DL) method designed for the automated segmentation of lung lobes in chest CT scans.

Approach

We introduce an automated DL-based approach that leverages anatomical information from the lung's vascular system to guide and enhance the segmentation process. This involves utilizing a lung vessel connectivity (LVC) map, which encodes relevant lung vessel anatomical data. Our study explores the performance of three different neural network architectures within the nnU-Net framework: a standalone U-Net, a multitasking U-Net, and a cascade U-Net.

Results

Experimental findings suggest that the inclusion of LVC information in the DL model can lead to improved segmentation accuracy, particularly, in the challenging boundary regions of expiration chest CT volumes. Furthermore, our study demonstrates the potential for LVC to enhance the model's generalization capabilities. Finally, the method's robustness is evaluated through the segmentation of lung lobes in 10 cases of COVID-19, demonstrating its applicability in the presence of pulmonary diseases.

Conclusions

Incorporating prior anatomical information, such as LVC, into the DL model shows promise for enhancing segmentation performance, particularly in the boundary regions. However, the extent of this improvement has limitations, prompting further exploration of its practical applicability.

A survey and taxonomy of 2.5D approaches for lung segmentation and nodule detection in CT images

CAD systems for lung cancer diagnosis and detection can significantly offer unbiased, infatiguable diagnostics with minimal variance, decreasing the mortality rate and the five-year survival rate. Lung segmentation and lung nodule detection are critical steps in the lung cancer CAD system pipeline. Literature on lung segmentation and lung nodule detection mostly comprises techniques that process 3-D volumes or 2-D slices and surveys. However, surveys that highlight 2.5D techniques for lung segmentation and lung nodule detection still need to be included. This paper presents a background and discussion on 2.5D methods to fill this gap. Further, this paper also gives a taxonomy of 2.5D approaches and a detailed description of the 2.5D approaches. Based on the taxonomy, various 2.5D techniques for lung segmentation and lung nodule detection are clustered into these 2.5D approaches, which is followed by possible future work in this direction.

A hybrid hemodynamic knowledge-powered and feature reconstruction-guided scheme for breast cancer segmentation based on DCE-MRI

Automatically and accurately annotating tumor in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which provides a noninvasive in vivo method to evaluate tumor vasculature architectures based on contrast accumulation and washout, is a crucial step in computer-aided breast cancer diagnosis and treatment. However, it remains challenging due to the varying sizes, shapes, appearances and densities of tumors caused by the high heterogeneity of breast cancer, and the high dimensionality and ill-posed artifacts of DCE-MRI. In this paper, we propose a hybrid hemodynamic knowledge-powered and feature reconstruction-guided scheme that integrates pharmacokinetics prior and feature refinement to generate sufficiently adequate features in DCE-MRI for breast cancer segmentation. The pharmacokinetics prior expressed by time intensity curve (TIC) is incorporated into the scheme through objective function called dynamic contrast-enhanced prior (DCP) loss. It contains contrast agent kinetic heterogeneity prior knowledge, which is important to optimize our model parameters. Besides, we design a spatial fusion module (SFM) embedded in the scheme to exploit intra-slices spatial structural correlations, and deploy a spatial-kinetic fusion module (SKFM) to effectively leverage the complementary information extracted from spatial-kinetic space. Furthermore, considering that low spatial resolution often leads to poor image quality in DCE-MRI, we integrate a reconstruction autoencoder into the scheme to refine feature maps in an unsupervised manner. We conduct extensive experiments to validate the proposed method and show that our approach can outperform recent state-of-the-art segmentation methods on breast cancer DCE-MRI dataset. Moreover, to explore the generalization for other segmentation tasks on dynamic imaging, we also extend the proposed method to brain segmentation in DSC-MRI sequence. Our source code will be released on https://github.com/AI-medical-diagnosis-team-of-JNU/DCEDuDoFNet.

A 2D-3D hybrid convolutional neural network for lung lobe auto-segmentation on standard slice thickness computed tomography of patients receiving radiotherapy

Background

Accurate segmentation of lung lobe on routine computed tomography (CT) images of locally advanced stage lung cancer patients undergoing radiotherapy can help radiation oncologists to implement lobar-level treatment planning, dose assessment and efficacy prediction. We aim to establish a novel 2D–3D hybrid convolutional neural network (CNN) to provide reliable lung lobe auto-segmentation results in the clinical setting.

Methods

We retrospectively collected and evaluated thorax CT scans of 105 locally advanced non-small-cell lung cancer (NSCLC) patients treated at our institution from June 2019 to August 2020. The CT images were acquired with 5 mm slice thickness. Two CNNs were used for lung lobe segmentation, a 3D CNN for extracting 3D contextual information and a 2D CNN for extracting texture information. Contouring quality was evaluated using six quantitative metrics and visual evaluation was performed to assess the clinical acceptability.

Results

For the 35 cases in the test group, Dice Similarity Coefficient (DSC) of all lung lobes contours exceeded 0.75, which met the pass criteria of the segmentation result. Our model achieved high performances with DSC as high as 0.9579, 0.9479, 0.9507, 0.9484, and 0.9003 for left upper lobe (LUL), left lower lobe (LLL), right upper lobe (RUL), right lower lobe (RLL), and right middle lobe (RML), respectively. The proposed model resulted in accuracy, sensitivity, and specificity of 99.57, 98.23, 99.65 for LUL; 99.6, 96.14, 99.76 for LLL; 99.67, 96.13, 99.81 for RUL; 99.72, 92.38, 99.83 for RML; 99.58, 96.03, 99.78 for RLL, respectively. Clinician's visual assessment showed that 164/175 lobe contours met the requirements for clinical use, only 11 contours need manual correction.

Conclusions

Our 2D–3D hybrid CNN model achieved accurate automatic segmentation of lung lobes on conventional slice-thickness CT of locally advanced lung cancer patients, and has good clinical practicability.

Artificial intelligence in radiology

The interest in artificial intelligence (AI) has ballooned within radiology in the past few years primarily due to notable successes of deep learning. With the advances brought by deep learning, AI has the potential to recognize and localize complex patterns from different radiological imaging modalities, many of which even achieve comparable performance to human decision-making in recent applications. In this chapter, we review several AI applications in radiology for different anatomies: chest, abdomen, pelvis, as well as general lesion detection/identification that is not limited to specific anatomies. For each anatomy site, we focus on introducing the tasks of detection, segmentation, and classification with an emphasis on describing the technology development pathway with the aim of providing the reader with an understanding of what AI can do in radiology and what still needs to be done for AI to better fit in radiology. Combining with our own research experience of AI in medicine, we elaborate how AI can enrich knowledge discovery, understanding, and decision-making in radiology, rather than replacing the radiologist.

Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans

Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural networks have achieved good performance for this task. However, they are still limited in capturing structured relationships due to the nature of convolution. The shape of the pulmonary lobes affect each other and their borders relate to the appearance of other structures, such as vessels, airways, and the pleural wall. We argue that such structural relationships play a critical role in the accurate delineation of pulmonary lobes when the lungs are affected by diseases such as COVID-19 or COPD. In this paper, we propose a relational approach (RTSU-Net) that leverages structured relationships by introducing a novel non-local neural network module. The proposed module learns both visual and geometric relationships among all convolution features to produce self-attention weights. With a limited amount of training data available from COVID-19 subjects, we initially train and validate RTSU-Net on a cohort of 5000 subjects from the COPDGene study (4000 for training and 1000 for evaluation). Using models pre-trained on COPDGene, we apply transfer learning to retrain and evaluate RTSU-Net on 470 COVID-19 suspects (370 for retraining and 100 for evaluation). Experimental results show that RTSU-Net outperforms three baselines and performs robustly on cases with severe lung infection due to COVID-19.

Automated labeling of the airway tree in terms of lobes based on deep learning of bifurcation point detection

This paper presents an automatic lobe-based labeling of airway tree method, which can detect the bifurcation points for reconstructing and labeling the airway tree from a computed tomography image. A deep learning-based network structure is designed to identify the four key bifurcation points. Then, based on the detected bifurcation points, the entire airway tree is reconstructed by a new region-growing method. Finally, with the basic airway tree anatomy and topology knowledge, individual branches of the airway tree are classified into different categories in terms of pulmonary lobes. There are several advantages in our method such as the detection of the bifurcation points does not depend on the segmentation of airway tree and only four bifurcation points need to be manually labeled for each sample to prepare the training dataset. The segmentation of airway tree is guided by the detected points, which overcomes the difficulty of manual seed selection of conventional region-growing algorithm. In addition, the bifurcation points can help analyze the tree structure, which provides a basis for effective airway tree labeling. Experimental results show that our method is fast, stable, and the accuracy of our method is 97.85%, which is higher than that of the traditional skeleton-based method. Graphical Abstract The pipeline of our proposed lobe-based airway tree labeling method. Given a raw CT volume, a neural network structure is designed to predict major bifurcation points of airway tree. Based on the detected points, airway tree is reconstructed and labeled in terms of lobes.

An Efficient Method for the Detection of Oblique Fissures from Computed Tomography images of Lungs

Detection of a pulmonary fissure in lungs is difficult due to its anatomical changeability among humans and it is essential in the clinical environment for accurate localizing and treating the lung abnormalities on a lobe level in human lungs. In this work, an algorithmic approach is proposed to detect the lung oblique fissures from lung computed tomography (CT) images. In the preprocessing module of our approach, the lung structures are enhanced using morphological operation and lung images are de-noised using Wiener filter. In the second module, lung regions are segmented using techniques, namely, thresholding and background subtraction. In the third module of our algorithm, initially, fissure regions are segmented using the active contour model, then by applying the rule based approach on the fissure regions, the oblique fissures are segmented. The proposed algorithm has been tested on 50 images collected from Lung Image Database Consortium (LIDC) and 30 images obtained from Early Lung Cancer Action Program (ELCAP).

Pulmonary lobe separation in expiration chest CT scans based on subject-specific priors derived from inspiration scans

Segmentation of pulmonary lobes in inspiration and expiration chest CT scan pairs is an important prerequisite for lobe-based quantitative disease assessment. Conventional methods process each CT scan independently, resulting typically in lower segmentation performance at expiration compared to inspiration. To address this issue, we present an approach, which utilizes CT scans at both respiratory states. It consists of two main parts: a base method that processes a single CT scan and an extended method that utilizes the segmentation result obtained on the inspiration scan as a subject-specific prior for segmentation of the expiration scan. We evaluated the methods on a diverse set of 40 CT scan pairs. In addition, we compare the performance of our method to a registration-based approach. On inspiration scans, the base method achieved an average distance error of 0.59, 0.64, and 0.91 mm for the left oblique, right oblique, and right horizontal fissures, respectively, when compared with expert-based reference tracings. On expiration scans, the base method's errors were 1.54, 3.24, and 3.34 mm, respectively. In comparison, utilizing proposed subject-specific priors for segmentation of expiration scans allowed decreasing average distance errors to 0.82, 0.79, and 1.04 mm, which represents a significant improvement ([Formula: see text]) compared with all other methods investigated.