Interactive segmentation of medical images through fully convolutional neural networks (1903.08205v1)

Abstract: Image segmentation plays an essential role in medicine for both diagnostic and interventional tasks. Segmentation approaches are either manual, semi-automated or fully-automated. Manual segmentation offers full control over the quality of the results, but is tedious, time consuming and prone to operator bias. Fully automated methods require no human effort, but often deliver sub-optimal results without providing users with the means to make corrections. Semi-automated approaches keep users in control of the results by providing means for interaction, but the main challenge is to offer a good trade-off between precision and required interaction. In this paper we present a deep learning (DL) based semi-automated segmentation approach that aims to be a "smart" interactive tool for region of interest delineation in medical images. We demonstrate its use for segmenting multiple organs on computed tomography (CT) of the abdomen. Our approach solves some of the most pressing clinical challenges: (i) it requires only one to a few user clicks to deliver excellent 2D segmentations in a fast and reliable fashion; (ii) it can generalize to previously unseen structures and "corner cases"; (iii) it delivers results that can be corrected quickly in a smart and intuitive way up to an arbitrary degree of precision chosen by the user and (iv) ensures high accuracy. We present our approach and compare it to other techniques and previous work to show the advantages brought by our method.

• The paper presents an interactive FCNN segmentation approach that minimizes manual input while iteratively refining results with high precision.
• It employs simple mouse clicks and Gaussian-smoothed guidance signals to adaptively optimize segmentation accuracy based on user feedback.
• Evaluations on CT images demonstrate that the method achieves superior Dice scores and robustly segments unseen structures like colon tumors.

Interactive Segmentation of Medical Images Using Fully Convolutional Neural Networks

The paper discusses a deep learning-based approach to semi-automated image segmentation, specifically in the domain of medical imaging. The focus is on developing a "smart" interactive tool that leverages fully convolutional neural networks (FCNNs) to address significant challenges in medical image segmentation. The proposed methodology offers a solution that balances the need for precision in image segmentation with the minimal required user interaction.

Overview

The authors categorize image segmentation methods into manual, semi-automatic, and fully-automatic. Manual segmentation, while precise, is time-consuming and subject to operator variability. Fully-automatic systems, although rapid and requiring no manual input, often deliver sub-optimal results and lack mechanisms for correcting errors. Semi-automatic approaches offer a compromise but can still struggle with achieving high accuracy without significant user input.

In response, this paper introduces a semi-automated segmentation approach that utilizes FCNNs to produce accurate segmentation outputs with minimal user interaction. The system's design inherently accommodates user error corrections interactively, allowing users to achieve a desired level of segmentation precision effectively. This methodology is demonstrated through the segmentation of multiple organs in CT images of the abdomen.

Methodology

The segmentation system hinges on user interaction implemented through simple mouse clicks. Users provide guidance by indicating regions of interest, thereby facilitating an iterative refinement process of the segmentation results. The network employs a hybrid approach to learning, combining both pre-trained scenarios and real-time user feedback during the segmentation task.

The architecture is based on existing encoder-decoder designs, augmented with Gaussian-smoothed guidance signals derived from user interactions. These interactions serve as additional input channels to the network, enhancing its ability to refine segmentations adaptively. Such a design not only anchors segmentation to user preferences but also empowers the system to generalize to previously unseen objects or complex structures.

Results

The system's performance was evaluated extensively on publicly available datasets demonstrative of varied medical imaging challenges. Notably, the system achieved high Dice scores on known and unknown regions alike, often surpassing state-of-the-art fully automated methods. The approach offers a particularly noteworthy capability to handle previously unseen structures, such as colon tumors, thus broadening its applicability in medical research and clinical settings.

The FCNN's ability to learn with limited data, facilitated by interactive correction, underscores its potential effectiveness over a broad spectrum of medical imaging tasks. Observations from the conducted experiments show that the performance benefits from more extensive interaction during the training phase. However, the capability for this model to maintain accuracy with increasing test-time interactions validates the model's robustness and adaptability.

Implications and Future Directions

The proposed interactive segmentation system demonstrates substantial promise for integration into clinical workflows, offering a tool that enhances the scalability and adaptability of medical imaging tasks. Its ability to facilitate interactive corrections aligns with the increasing demand for precision in clinical diagnostics and personalized patient care.

The methodology, while validated on CT images, is anticipated to extend to other imaging modalities, such as MRI, due to its flexible design. Future developments could focus on reducing interaction duration further while maintaining accuracy, enhancing model efficiency even in off-the-shelf computing environments.

Moreover, expanding this approach to accommodate a hierarchy of anatomical structures and variations, possibly including pathological zones, could profoundly impact the imaging domain, paving the way for more nuanced diagnostic and interventional practices. The integration of such systems into routine radiological practices can lead to enhanced patient outcomes due to more precise and reliable imaging interpretations.