A hybrid inductive learning-based and deductive reasoning-based 3-D path planning method in complex environments

Segato, Alice; Calimeri, Francesco; Irene, Testa; Corbetta, Valentina; Riva, Marco; Momi, Elena De

doi:10.1007/s10514-022-10042-z

Cited by 3 publications

(3 citation statements)

References 63 publications

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“…The most suitable ways to reach the target in 2D and 3D were found by paying attention to the needle kinematics without touching these obstacles from the determined entrance area. In 2022, Sagato et al 19 proposed to combine deductive techniques that select the preferred paths with inductive techniques that produce path candidates. An inductive learning model is specifically trained KURT PEHLIVANOĞLU ET AL.…”

Section: Previous Surgical Path Planning Methods For Neurosurgerymentioning

confidence: 99%

“…In 2022, Sagato et al. 19 proposed to combine deductive techniques that select the preferred paths with inductive techniques that produce path candidates. An inductive learning model is specifically trained using specialist demonstrations and rules converted into a reward function when logic programming is employed to select the starting point following recommendations from some domain experts.…”

Section: Introductionmentioning

confidence: 99%

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A new surgical path planning framework for neurosurgery

Kurt Pehlivanoğlu,

Ay,

Eker

et al. 2023

Robotics Computer Surgery

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BackgroundDespite using a variety of path‐finding algorithms that use tracts, the most significant advancement in this study is considering the values of all brain areas by doing atlas‐based segmentation for a more precise search. Our motivation comes from the literature’s shortcomings in designing and implementing path‐planning methods. Since planning paths with curvatures is a complex problem that requires considering many surgical and physiological constraints, most path‐planning strategies focus on straight paths. There is also a lack of studies that focus on the complete structure of the brain with the tracks, veins, and segmented areas. Instrument dependence is another inadequacy of the methods proposed in the literature.AimsThe aim of this study is to design a new surgical path planning framework that helps to plan the surgical path independently of the instrument, considers the entire structure of the brain, and allows curvilinear surgical paths. Thus, neurosurgeons can generate patient‐specific possible optimal surgical pathways before the neurosurgical procedure.Materials & MethodsThe proposed framework includes different path‐finding algorithms (Dijkstra, A*, and their aggressive variants) that find optimal paths by taking the risk scores (surgeons assessed all the segmented regions, considering the extent of damage. In this evaluation, scores ranged from “0 to 10,” with the most critical areas receiving a score of “10,” while the least possible affected areas were assigned a score of "0") for sensitive brain areas into consideration. For the tract image processing the framework includes fractional anisotropy (FA), relative anisotropy (RA), spherical measure (SM), and linear measure (LM) methods.ResultsThis is the first paper to handle tracts and atlas‐based segmentation of the human brain altogether under a framework for surgical path planning. The framework has a dynamic structure that gives the flexibility to add different path‐finding algorithms and generate different widths of surgical pathways. Moreover, surgeons can update the score table to guarantee minimally invasive surgery. The output file format of all the extracted surgical paths is NRRD, so it can be easily visualised, analysed, or processed over the third part software tools.DiscussionIn this study, we generated many possible surgical pathways then these pathways were evaluated by the surgeons the results were impressive because the framework could identify surgical pathways used in real‐world surgery that correspond to the standard pathways such as anterior transsylvian, trans sulcal, transgyral, and sub‐temporal.ConclusionThis study proposes a new surgical path planning framework for neurosurgery. Moreover, in the future by adding/adopting different parameters (such as operation time, and short and long‐term complications after surgery) to the proposed framework, it would be possible to find new surgical pathways for difficult surgical conditions.

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Section: Previous Surgical Path Planning Methods For Neurosurgerymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new surgical path planning framework for neurosurgery

Kurt Pehlivanoğlu,

Ay,

Eker

et al. 2023

Robotics Computer Surgery

View full text Add to dashboard Cite

show abstract

“…Segato et al. [ 8 ] proposed a learning-based planning algorithm using the inductive learning and deductive reasoning framework, which can derive optimal insertion paths. However, their planning scheme is only suitable for programmable needles without need to consider rotations of the needle.…”

Section: Introductionmentioning

confidence: 99%

A Heuristically Accelerated Reinforcement Learning-Based Neurosurgical Path Planner

Gao

Zhang

et al. 2023

Cyborg Bionic Syst

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The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain; with proper path planning, it can also minimize the potential damage by setting constraints and optimizing the insertion path. Recently, reinforcement learning (RL)-based path planning algorithm has shown promising results in neurosurgery, but because of the trial and error mechanism, it can be computationally expensive and insecure with low training efficiency. In this paper, we propose a heuristically accelerated deep Q network (DQN) algorithm to safely preoperatively plan a needle insertion path in a neurosurgical environment. Furthermore, a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL algorithm. Simulations are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN algorithms. Tests showed promising results of our algorithm in saving over 50 training episodes, calculating path lengths of 0.35 after normalization, which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm, respectively. Moreover, the maximum curvature during planning is reduced to 0.046 from 0.139 mm −1 using the proposed algorithm compared to DQN.

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A review on tissue-needle interaction and path planning models for bevel tip type flexible needle minimal intervention

Muzzammil,

Zhang,

Ejaz

et al. 2023

MBE

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<abstract> <p>A flexible needle has emerged as a crucial clinical technique in contemporary medical practices, particularly for minimally invasive interventions. Its applicability spans diverse surgical domains such as brachytherapy, cardiovascular surgery, neurosurgery and others. Notably, flexible needles find utility in biopsies requiring deep skin penetration to access infected areas. Despite its minimally invasive advantages, the precise guidance of the needle to its intended target, while avoiding damage to bones, blood vessels, organs and tissues, remains a significant challenge for researchers. Consequently, extensive research has been dedicated to enhancing the steering and accuracy of flexible needles. Here, we aim to elucidate the recent advancements, trends and perspectives in flexible needle steering models and path planning over the last 15 years. The discussed models encompass various types, including symmetric-tip needles, curved-tip needles, tendon-actuated needles, programmable needles and the innovative fracture-directed waterjet needles. Moreover, the paper offers a comprehensive analysis, comparing the trajectories followed by these needle models to attain the desired target with minimal tissue damage. By delving into these aspects, the paper contributes to a deeper understanding of the current landscape of flexible needle technology and guides future research directions in this dynamic field.</p> </abstract>

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A hybrid inductive learning-based and deductive reasoning-based 3-D path planning method in complex environments

Cited by 3 publications

References 63 publications

A new surgical path planning framework for neurosurgery

A new surgical path planning framework for neurosurgery

A Heuristically Accelerated Reinforcement Learning-Based Neurosurgical Path Planner

A review on tissue-needle interaction and path planning models for bevel tip type flexible needle minimal intervention

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