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

Bragman, Felix J. S.; McClelland, Jamie R.; Jacob, Joseph; Hurst, John R.; Hawkes, David J.

doi:10.1109/tmi.2017.2688377

Cited by 30 publications

(20 citation statements)

References 32 publications

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“…The results are shown in Table 2. Our method achieved an overall Dice score of 0.934, which is very competitive with the state-of-the-art [2] with a Dice score of 0.938, while outperforming the methods of Giuliani et al [6] and van Rikxoort et al [11].…”

Section: Baselines For Comparisonmentioning

confidence: 69%

See 1 more Smart Citation

Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network

Imran

Hatamizadeh

Ananth³

et al. 2018

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

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Reliable and automatic segmentation of lung lobes is important for diagnosis, assessment, and quantification of pulmonary diseases. The existing techniques are prohibitively slow, undesirably rely on prior (airway/vessel) segmentation, and/or require user interactions for optimal results. This work presents a reliable, fast, and fully automated lung lobe segmentation based on a progressive dense V-network (PDV-Net). The proposed method can segment lung lobes in one forward pass of the network, with an average runtime of 2 seconds using 1 Nvidia Titan XP GPU, eliminating the need for any prior atlases, lung segmentation or any subsequent user intervention. We evaluated our model using 84 chest CT scans from the LIDC and 154 pathological cases from the LTRC datasets. Our model achieved a Dice score of 0.939 ± 0.02 for the LIDC test set and 0.950 ± 0.01 for the LTRC test set, significantly outperforming a 2D U-net model and a 3D dense V-net. We further evaluated our model against 55 cases from the LOLA11 challenge, obtaining an average Dice score of 0.935-a performance level competitive to the best performing team with an average score of 0.938. Our extensive robustness analyses also demonstrate that our model can reliably segment both healthy and pathological lung lobes in CT scans from different vendors, and that our model is robust against configurations of CT scan reconstruction.

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Section: Baselines For Comparisonmentioning

confidence: 69%

“…There is no correlation between nodule volume and lobe segmentation accuracy, found from Pearson correlation. Overall 0.9345 [6] 0.9282 [2] 0.9384 [11] 0.9195 3 *Jaccard score to Dice score conversion: Dice = 2 × Jaccard/(1 + Jaccard)…”

Section: Baselines For Comparisonmentioning

confidence: 99%

Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network

Imran

Hatamizadeh

Ananth³

et al. 2018

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

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“…It has a variety of characteristics and merits. First, multi-section vector information was used for pulmonary fissure enhancement, whereas many existing methods [12,13,22] use only the magnitude information to enhance pulmonary fissures, which cannot efficiently distinguish between fissures and clutters. Second, an improved orientation partition scheme was presented to suppress clutters.…”

Section: Discussionmentioning

confidence: 99%

Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model

Xiao

Zhou

2019

Algorithms

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As a typical landmark in human lungs, the detection of pulmonary fissures is of significance to computer aided diagnosis and surgery. However, the automatic detection of pulmonary fissures in CT images is a difficult task due to complex factors like their 3D membrane shape, intensity variation and adjacent interferences. Based on the observation that the fissure object often appears as thin curvilinear structures across 2D section images, we present an efficient scheme to solve this problem by merging the fissure line detection from multiple cross-sections in different directions. First, an existing oriented derivative of stick (ODoS) filter was modified for pulmonary fissure line enhancement. Then, an orientation partition scheme was applied to suppress the adhering clutters. Finally, a multiple section model was proposed for pulmonary fissure integration and segmentation. The proposed method is expected to improve fissure detection by extracting more weak objects while suppressing unrelated interferences. The performance of our scheme was validated in experiments using the publicly available open Lobe and Lung Analysis 2011 (LOLA11) dataset. Compared with manual references, the proposed scheme achieved a high segmentation accuracy, with a median F1-score of 0.8916, which was much better than conventional methods.

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“…In addition to utilizing CT density information for segmentation of fissures, other relevant anatomical information (e.g., airways and vessels) is also frequently considered to increase robustness, and different approaches have been proposed. 2,4,9 We follow such an approach, but this type of information alone is typically not sufficient to achieve good performance on expiration scans. Because we address this issue by utilizing a subject-specific prior (Sec.…”

Section: Cost Function Designmentioning

confidence: 99%

“…6,7 A different approach was used by van Rikxoort et al 8 From an atlas, they propose to select a reference dataset, which best describes the dataset to be segmented. Bragman et al 9 presented a method to derive an average fissure prior in form of a confidence map from unlabeled training data. However, the shape of individual lobar boundaries can show large anatomical variation.…”

Section: Introductionmentioning

confidence: 99%

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

2018

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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.

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

Cited by 30 publications

References 32 publications

Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network

Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network

Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model

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

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