Description:[001] The present invention pertains generally to the field of biomedical imaging. More specifically, this invention relates to a computer-implemented method designed to segment and differentiate specific regions or structures within biomedical images. This method seeks to improve the accuracy, reliability, and efficiency of identifying, categorizing, and delineating various anatomical or pathological features within a wide range of biomedical images, such as those obtained through magnetic resonance imaging (MRI), computed tomography (CT), X-ray, ultrasound, and other imaging modalities. The system and method have particular relevance to healthcare professionals, researchers, and clinicians for diagnostic, therapeutic, and research applications.
BACKGROUND OF THE INVENTION
[002] The following description provides the information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] Further, the approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
[004] The process of obtaining, interpreting, and analyzing biomedical images has been fundamental to advancements in modern medicine. Historically, professionals have relied heavily on manual techniques to differentiate between various structures and regions in these images, a task that is both time-consuming and prone to human error. As biomedical imaging techniques, like MRI and CT scans, have become more sophisticated, the sheer volume of information contained within each image has also increased dramatically. This means that each image not only contains more data but also presents more intricate details that demand precise segmentation.
[005] The importance of accurate image segmentation cannot be overstated. Segmentation plays a crucial role in diagnosing diseases, planning surgical procedures, and determining treatment paths. For instance, the delineation of a tumor's boundaries from surrounding healthy tissue in an MRI can greatly impact a physician's treatment recommendations and prognostic evaluations. Likewise, in the realm of research, accurate segmentation can lead to better understandings of anatomical structures, disease progression, and treatment responses.
[006] However, with the advent of digital technology, there has been a pressing need to automate and refine the segmentation process. Early computer-based methods for image segmentation often operated on fixed thresholds or basic edge detection techniques. While these methods were a step forward, they were also limited in their ability to handle the complexity and variability of biomedical images. Variations in tissue contrast, noise interference, and the diverse nature of anatomical structures often resulted in sub-optimal segmentations.
[007] The rise of machine learning and artificial intelligence offered a potential solution. Leveraging these technologies, newer methods aimed to train models on vast datasets, allowing them to recognize and segment structures in biomedical images with greater precision. However, this approach also brought about challenges, including the need for large labeled datasets, the risk of model overfitting, and the computational demands of training sophisticated models.
[008] The invention was born out of a holistic assessment of the challenges faced by the biomedical community. Understanding that the variability of biological structures is vast, this new method sought to ensure that it could handle differences not only across individual patients but also across different imaging modalities. A one-size-fits-all approach simply wasn't sufficient. From the granularity of cellular structures in microscopic images to the broader landscape of organs in an MRI, the solution needed to be adaptable.
[009] Furthermore, it was recognized that while automation was the goal, the role of the human expert could not be entirely dismissed. The wealth of knowledge and intuition that radiologists, pathologists, and other specialists bring to the table is invaluable. Therefore, the new method aimed to bridge the gap between automated processes and human expertise, allowing for collaboration and refinement of results when necessary. This was a significant deviation from earlier methods that were either too manual or too automated, without a clear pathway for the integration of both.
[010] In addition, as the biomedical community became increasingly global, the need for a universally applicable solution became evident. The invention, therefore, was designed with interoperability in mind. The segmented images, irrespective of the source or the modality, needed to be comprehensible and usable by any medical professional around the world, ensuring that patients everywhere could benefit from the advancements this method promised.
[011] The scalability of the method was another area of focus. With the global generation of biomedical images increasing at an exponential rate, it was clear that the solution had to handle large volumes of data efficiently. But it wasn't just about quantity; the quality of the segmentation was paramount. The invention, therefore, incorporated advanced algorithms, some of which harnessed the power of deep learning, to ensure that as the speed of segmentation increased, the accuracy did not diminish.
[012] Finally, the environmental and economic footprint of the invention was considered. The vast computational power required for advanced image processing often results in significant energy consumption. The inventors were mindful of this, aiming to optimize the algorithmic efficiency of the method. This not only reduced energy demands but also made the process more cost-effective, lowering barriers to adoption, especially in settings with limited resources. Some patent prior art related to proposed invention mentioned below.
[013] Title: Method and system for medical image segmentation using deformable models
Summary: Describes a system and method that utilizes deformable models to segment medical images. It mentions the use of internal and external forces to refine segmentation boundaries.
Relevance: Directly relates to medical image segmentation. The method, however, seems focused on a specific algorithm involving deformable models.
Title: Method for segmenting medical images and detecting surface anomalies in anatomical structures
[014] Title: Semi-automatic segmentation of medical images
Summary: A method combining automatic segmentation algorithms with user inputs to refine the segmentation results.
Relevance: Directly relevant due to the emphasis on computer-assisted methods, hinting at a balance between automation and human expertise.
[015] Title: Neural network-based method for medical image segmentation
Summary: Describes a method using neural networks, specifically convolutional neural networks (CNNs), to segment medical images.
Relevance: Highly relevant due to the use of machine learning models, which could be similar or foundational to the proposed invention.
[016] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[017] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.

SUMMARY OF THE PRESENT INVENTION
[018] The proposed invention introduces a computer-implemented technique for the intricate task of segmenting biomedical images derived from an array of imaging modalities. At its core, this innovation harnesses the prowess of advanced machine learning algorithms, with a particular emphasis on deep learning models like convolutional neural networks, to autonomously identify and demarcate distinct anatomical and pathological structures within these images.
[019] Recognizing the pivotal role of medical expertise, the method intertwines automation with human intervention, facilitated through an intuitive interactive interface. Users are empowered to fine-tune and adjust the automated segmentation outputs, ensuring that the results benefit from a harmonized blend of machine precision and human intuition. Going beyond mere segmentation, the system is laden with auxiliary features to enhance user experience and outcomes.
[020] A real-time feedback mechanism allows for immediate adjustments, while a focus on data security ensures the utmost confidentiality and integrity of the sensitive biomedical images. Furthermore, the system's cloud compatibility ensures scalability and fosters collaborative interpretations of the segmented images. In essence, this invention promises a paradigm shift in biomedical image segmentation by offering a solution that seamlessly merges technological advancements with the invaluable insights of medical professionals.
[021] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[022] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[023] When considering the following thorough explanation of the present invention, it will be easier to understand it and other objects than those mentioned above will become evident. Such description refers to the illustrations in the annex, wherein:
[024] FIG. 1, illustrates a general functional working diagram, in accordance with an embodiment of the present invention.
[025] FIG. 2, illustrates a concept of the functional flow diagram, accordance with an embodiment of the present invention.in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[026] The following sections of this article will provide various embodiments of the current invention with references to the accompanying drawings, whereby the reference numbers utilised in the picture correspond to like elements throughout the description. However, this invention is not limited to the embodiment described here and may be embodied in several other ways. Instead, the embodiment is included to ensure that this disclosure is extensive and complete and that individuals of ordinary skill in the art are properly informed of the extent of the invention.
[027] Numerical values and ranges are given for many parts of the implementations discussed in the following thorough discussion. These numbers and ranges are merely to be used as examples and are not meant to restrict the claims' applicability. A variety of materials are also recognised as fitting for certain aspects of the implementations. These materials should only be used as examples and are not meant to restrict the application of the innovation.
[028] Referring now to the drawings, these are illustrated in FIG. 1&2, The proposed invention pertains to a cutting-edge computer-implemented method designed explicitly for the segmentation of biomedical images. In the ever-evolving landscape of medical diagnostics and research, the ability to discern and demarcate specific regions or structures within biomedical images plays a paramount role. However, given the intricate nature of such images, which may arise from various modalities like MRI, CT scans, X-rays, ultrasounds, and more, achieving precise segmentation has historically been a complex endeavor. This is where the presented invention comes to the fore, aiming to reshape the standards of precision, efficiency, and adaptability in biomedical image segmentation.
[029] The method at the core of this invention integrates advanced algorithms, some of which are rooted in the principles of machine learning and artificial intelligence. By training on vast and diverse datasets, these algorithms are capable of recognizing and delineating the nuances of diverse anatomical structures and potential pathological features. Moreover, the system's versatility allows it to adapt to the unique characteristics of different imaging modalities, ensuring consistent performance regardless of whether it's processing an X-ray of a limb or an MRI of the brain.
[030] One notable attribute of this invention is its emphasis on balancing automation with human expertise. Recognizing the invaluable insight that medical professionals bring to the table, the method is designed to work in tandem with human intervention when needed. The system provides a platform for healthcare professionals to refine and adjust the automated segmentation results, ensuring that the final output benefits from both machine precision and human intuition.
[031] To complement its core segmentation capabilities, the invention also boasts a robust suite of features designed to enhance user experience and outcomes. An interactive interface offers users a visual insight into the segmentation process, and real-time feedback mechanisms ensure prompt adjustments can be made when necessary. Furthermore, advanced optimization techniques have been incorporated, ensuring that the method remains efficient and rapid even when dealing with voluminous datasets or high-resolution images.
[032] Beyond its technical prowess, the invention also addresses broader challenges in the biomedical community. It's designed to be interoperable, ensuring that segmented images are universally comprehensible, thereby fostering a more collaborative and holistic approach to medical diagnostics and research worldwide. Furthermore, algorithmic efficiency has been optimized to reduce energy consumption, making the system not just cost-effective but also environmentally conscious.
[033] The underpinnings of this invention are firmly rooted in the synthesis of data science and medical understanding. While the technological advancements play a crucial role, the incorporation of medical expertise during the design phase ensures that the tool remains clinically relevant, addressing real-world challenges faced by healthcare professionals. The collaborative ethos of this invention transcends typical segmentation methods, elevating it from a mere tool to a comprehensive solution tailored for the biomedical community.
[034] Moreover, the invention's adaptability extends beyond imaging modalities. It's primed to seamlessly integrate with emerging imaging technologies, ensuring its long-term relevance in an industry known for rapid technological evolution. By adopting a modular architecture, the system allows for the inclusion of new algorithms or techniques without necessitating an overhaul of the existing framework. This forward-thinking approach ensures that as the nuances of biomedical imaging evolve, so too will the capabilities of this system, preventing obsolescence and ensuring longevity.
[035] User engagement is another area where the invention excels. Recognizing the potential learning curve associated with new technologies, especially in fields as specialized as biomedical imaging, the system is equipped with a series of training and tutorial modules. These modules are designed to facilitate a smoother transition for users, from understanding the basic functionalities to mastering advanced features. Additionally, to foster a sense of community and collaborative development, the system incorporates a feedback loop where users can share insights, challenges, and potential areas for improvement. This not only aids in the continual refinement of the system but also ensures that it remains attuned to the ever-evolving needs of its user base.
[036] Security and data privacy, given the sensitive nature of biomedical images, have also been given paramount importance in the invention's design. Employing state-of-the-art encryption protocols and ensuring compliance with global data protection regulations, the system guarantees that the integrity and confidentiality of patient data are upheld at all times. This is vital not just from an ethical standpoint but also to instill trust and confidence among its users.
Furthermore, the system's compatibility with cloud-based platforms offers additional layers of flexibility and scalability. This ensures that institutions, regardless of size, can deploy and benefit from the invention without being burdened by infrastructural limitations. Such cloud integration also paves the way for collaborative projects, where teams from diverse geographical locations can work together, pooling their expertise to interpret complex biomedical images.
, Claims:1. A computer-implemented method for biomedical image segmentation, the method comprising:
a. receiving a biomedical image from one of multiple imaging modalities;
b. preprocessing said image to enhance contrast and reduce noise;
c. applying a machine learning algorithm trained on a diverse dataset to segment distinct structures within said image;
d. allowing user intervention to refine and adjust the automated segmentation results;
e. outputting the segmented image with demarcated boundaries for each segmented structure.
2. The method of claim 1, wherein the machine learning algorithm is a deep learning model comprising convolutional neural networks (CNNs).
3. The method of claim 1, further comprising an interactive interface that visually represents the segmentation process in real-time.
4. The method of claim 1, wherein the preprocessing step involves adaptive histogram equalization and Gaussian filtering.
5. The method of claim 1, wherein the diverse dataset includes images from MRI, CT scans, X-rays, ultrasounds, and other imaging modalities.
6. The method of claim 1, further comprising a feedback loop that allows users to share insights and potential areas for improvement within the system.
7. The method of claim 1, wherein the system employs advanced encryption protocols to ensure the security and confidentiality of the biomedical images.
8. The method of claim 1, wherein the system is compatible with cloud-based platforms, allowing for storage, analysis, and collaborative interpretation of biomedical images.
9. The method of claim 1, wherein the segmentation results are stored in a universally comprehensible format to facilitate interoperability across different medical software platforms.
10. The method of claim 1, wherein the machine learning algorithm utilizes transfer learning to improve efficiency and adaptability based on previous segmentation tasks.