Department Publications

3D reconstruction of the liver for volumetry is important for qualitative analysis and disease diagnosis. Liver volumetry using Ultrasound (US) scans, although advantageous due to less acquisition time and safety, is challenging due to the inherent noisiness in US scans, blurry boundaries, and partial liver visibility. We address these challenges by using the segmentation masks of a few incomplete sagittal-plane US scans of the liver in conjunction with a Statistical Shape Model (SSM) built using a set of CT scans of the liver. We compute the shape parameters needed to warp this canonical SSM to fit the US scans through a parametric regression network. The resulting 3D liver reconstruction is accurate and leads to automatic liver volume calculation. We evaluate the accuracy of the estimated liver volumes with respect to CT segmentation volumes using RMSE. Our volume computation is statistically much closer to the …

With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. Despite significant advances in fast multiplexed imaging sensors and deep-learning-based inverse solvers, the throughput of QPM is currently limited by the pixel-rate of the image sensors. Complementarily, to improve throughput further, here we propose to acquire images in a compressed form so that more information can be transferred beyond the existing hardware bottleneck of the image sensor. To this end, we present a numerical simulation of a learnable optical compression-decompression framework that learns content-specific features. The proposed differentiable quantitative phase microscopy (∂-QPM) first uses learnable optical processors as image compressors. The intensity representations produced by these optical processors are then captured by the …

With applications in quantitative metabolomics and label-free digital pathology, Quantitative Phase Microscopy (QPM) measures refractive index maps of thin transparent specimens like live cells or tissue sections. In QPM, refractive index maps are usually reconstructed from interference measurements of the object’s light field with respect to a reference field. To this end, many previous works focused on designing stable full-field interferometers from the bottom up. In this work, we present an alternative strategy to design a QPM system top-down, starting from the desired measurement outcomes, with no explicit knowledge about interferometry. We call our inverse design strategy Differentiable Microcopy. To this end, our Differentiable Microcopy approach designed a range of Fourier-filter-based QPM systems that do not require computational post-reconstruction. Our designs are superior to existing similar designs in …

Recently, various optical neural architectures have been designed to perform imaging and all-optical image processing. These designs consist of several optical masks with optimized transmission coefficients for the task. Designing sparse optical masks for them is important as it can promote different aspects of the design such as ease of fabrication and power efficiency of the design. To this end, inspired by the sparse filter designs, we propose training optical neural architectures with a regularization promoting sparsity in the masks. As preliminary results, we demonstrate a D2NN design for QPI achieving a sparsity of 33% with a performance degradation of only 12%.

Minimally invasive liver cancer treatments are increasingly favoured for their reduced risks and improved patient outcomes. However, real-time guidance during liver ablation remains challenging due to limited resources and the reliance on ultrasound (US) imaging. This project introduces a deep learning-based approach for 2D US to 3D Computed Tomography (CT) image registration, eliminating the need for 3D tracking systems. By integrating self-supervised feature embeddings and matching point estimation methods, our approach enhances real-time visualization of the liver, improving surgical precision and cost-effectiveness. With around 0.99 Normalized Cross-Correlation (NCC) value and 25.48-pixel distance in US and CT image registration, this research promises significant advancements in liver tumor ablation procedures and holds potential for broader applications in other ablation therapies.

Since its introduction, diffractive deep neural networks (D2NN) have been an emerging technology with many useful applications, such as 3D object recognition, saliency segmentation, and quantitative phase microscopy. However, fabricating D2NNs operating in the visible range has not been performed due to the complexities of fabricating nano-scale elements. Recent advancements such as Implosion Fabrication have made it possible to fabricate such networks using a discrete number of phase weights. We propose a quantization-aware training approach for D2NNs through modeling the quantization process in a differentiable manner using a sigmoid-based quantization function, facilitating the fabrication process. We also propose an efficient training schedule to guide the optimization process to converge to a better minima despite the limited number of quantization levels. Our method is simulated and …

We propose a novel attention-based deep learning framework for reconstructing images from compressively sensed measurements. Our method leverages knowledge of the sampling basis and structured sampling patterns, highlighting the importance of learning adaptive features from measurements.

Compressive sensing (CS) reconstructs images from sub-Nyquist measurements by solving a sparsity-regularized inverse problem. Traditional CS solvers use iterative optimizers with hand crafted sparsifiers, while early data-driven methods directly learn an inverse mapping from the low-dimensional measurement space to the original image space. The latter outperforms the former, but is restrictive to a pre-defined measurement domain. More recent, deep unrolling methods combine traditional proximal gradient methods and data-driven approaches to iteratively refine an image approximation. To achieve higher accuracy, it has also been suggested to learn both the sampling matrix, and the choice of measurement vectors adaptively. Contrary to the current trend, in this work we hypothesize that a general inverse mapping from a random set of compressed measurements to the image domain exists for a given measurement basis, and can be learned. Such a model is single-shot, non-restrictive and does not parametrize the sampling process. To this end, we propose MOSAIC, a novel compressive sensing framework to reconstruct images given any random selection of measurements, sampled using a fixed basis. Motivated by the uneven distribution of information across measurements, MOSAIC incorporates an embedding technique to efficiently apply attention mechanisms on an encoded sequence of measurements, while dispensing the need to use unrolled deep networks. A range of experiments validate our proposed architecture as a promising alternative for existing CS reconstruction methods, by achieving the state-of-the-art for metrics of …

With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. However, the speed of current QPM systems is limited by electronic hardware. To improve throughput further, here we propose differentiable optical-electronic quantitative phase microscopy (∂μ) that acquires images in a compressed form such that more information can be transferred beyond the electronic hardware bottleneck. The proposed microscopy uses optical feature extractors as image compressors. The resultant intensity distribution is then decompressed into QPM images by a reconstruction network. By optimizing optical-electronic parameters in an end-to-end manner, our method can improve the QPI throughput from Hours to Seconds (more than an order of magnitude).

Ever since the first microscope by Zacharias Janssen in the late 16th century, scientists have been inventing new types of microscopes for various tasks. Inventing a novel architecture demands years, if not decades, worth of scientific experience and creativity. In this work, we introduce Differentiable Microscopy (∂ µ), a deep learning-based design paradigm, to aid scientists design new interpretable microscope architectures. Differentiable microscopy first models a common physics-based optical system however with trainable optical elements at key locations on the optical path. Using pre-acquired data, we then train the model end-to-end for a task of interest. The learnt design proposal can then be simplified by interpreting the learnt optical elements. As a first demonstration, based on the optical 4-f system, we present an all-optical quantitative phase microscope (QPM) design that requires no computational post-reconstruction. A follow-up literature survey suggested that the learnt architecture is similar to the generalized phase concept developed two decades ago. We then incorporate the generalized phase contrast concept to simplify the learning procedure. Furthermore, we also demonstrate that similar results can be achieved by an uninterpretable learning based method, namely diffractive deep neural networks (D2NN). We outperform the existing benchmark for all-optical phase-to-intensity conversion on multiple datasets, and ours is the first demonstration of its kind on D2NNs. The proposed differentiable microscopy framework supplements the creative process of designing new optical systems and would perhaps lead to unconventional …

Designing new optical systems from the ground up for microscopy imaging tasks such as phase retrieval, requires substantial scientific expertise and creativity. To augment the traditional design process, we propose differentiable microscopy ( ), which introduces a top-down design approach. Using all optical phase retrieval as an illustrative example, we demonstrate the effectiveness of data-driven microscopy design through . Furthermore, we conduct comprehensive comparisons with existing all-optical phase retrieval methods, showcasing the consistent superiority of our learned designs across multiple datasets, including biological samples. To substantiate our ideas, we experimentally validate the functionality of one of the learned designs, providing a proof of concept. The proposed differentiable microscopy framework supplements the creative process of designing new phase microscopy systems and may be extended to other similar applications in optical design.

With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. Despite significant advances in fast multiplexed imaging sensors and deep-learning-based inverse solvers, the throughput of QPM is currently limited by the speed of electronic hardware. Complementarily, to improve throughput further, here we propose to acquire images in a compressed form such that more information can be transferred beyond the existing electronic hardware bottleneck. To this end, we present a learnable optical compression-decompression framework that learns content-specific features. The proposed differentiable quantitative phase microscopy ( ) first uses learnable optical feature extractors as image compressors. The intensity representation produced by these networks is then captured by the imaging sensor. Finally, a reconstruction network running on electronic hardware decompresses the QPM images. In numerical experiments, the proposed system achieves compression of 64 while maintaining the SSIM of and PSNR of dB on cells. The results demonstrated by our experiments open up a new pathway for achieving end-to-end optimized (i.e., optics and electronic) compact QPM systems that may provide unprecedented throughput improvements.

To improve spatial resolution in the PET-laparoscope system for intraoperative metastatic lymph nodes detection, design of a flat panel detector was optimized using Monte Carlo simulation tools. The performance of the new fixed detector was evaluated in point radio-source and phantom experiments. The experiment result shows that the system with the new fixed detector design has a spatial resolution better than 10 mm in three dimensions, and the system can distinguish two radio-sources clearly in a 2-minute scan.

Purpose Conventional positron emission tomography can be used only preoperatively to identify lymph node metastases, and hence, these growths are difficult to locate intraoperatively. Previously, an intraoperative laparoscopic coincidence imaging system, with an external fixed detector array and a detector probe that can be moved and inserted into the stomach, was proposed to identify lymph node metastases during stomach cancer surgery. This paper proposes a prototype detector for this system. Methods GAGG:Ce (Gd 3 Al 2 Ga 3 O 12 :Ce) scintillator crystals, one to one coupled with silicon photomultipliers (SiPMs), were used to identify 511 keV photons. An optical tracking system followed the position and orientation of the movable detector. SiPM outputs were read out by time-over-threshold (TOT)-based application-specific integrated circuits, which converted the electrical charges into digital pulses. To identify …

Intraoperative PET Navigation System for Cancer Surgery Toggle navigation J-STAGE home 資料・記事を探す 資料を探す:資料タイトルから 資料を探す:分野から 資料を探す:発行機関 から 記事を探す データを探す(J-STAGE Data) J-STAGEについて J-STAGEの概要 各種サービス・機能 公開データ 利用規約・ポリシー 新規登載の申し込み ニュース&PR お知らせ一覧 リリースノート メンテナンス情報 イベント情報 J-STAGEニュース 特集コンテンツ 各種広報媒体 サポート J-STAGE登載 機関用コンテンツ 登載ガイドライン・マニュアル 閲覧者向けヘルプ 動作確認済みブラウザ FAQ お 問い合わせ サイトマップ サインイン カート JA English 日本語 資料・記事を探す 資料を探す:資料 タイトルから 資料を探す:分野から 資料を探す:発行機関から 記事を探す データを探す(J-STAGE Data) J-STAGEについて J-STAGEの概要 各種サービス・機能 公開データ 利用規約・ポリシー 新規登載の申し込み ニュース&PR お知らせ一覧 リリースノート メンテナンス情報 イベント情報 J-STAGE…

Intraoperative PET-laparoscope system has been proposed to locate lymph node metastasis during gastric cancer surgery. The proposed system consists of an external detector array connected to a patient’s surgical bed and a movable detector probe which is inserted into the patient’s stomach along with a laparoscope. In this study a prototype detector for the PET-laparoscope system is presented with a pixelated movable detector probe tracked by an optical tracking system. Detector prototype reconstructed images of a 22 Na source with 3 mm spatial resolution in X (coronal) and Z (longitudinal) directions in a 2 min scan. The use of the pixelated multi-layer movable detector probe improved the spatial resolution of the system.

The PET-laparoscope system has been proposed to diagnose lymph node metastases intraoperatively in gastric cancer surgeries. The system uses two detector units, a fixed detector array and a movable detector probe to acquire PET images. The detector probe can be inserted into a patient’s stomach through laparoscopic ports. A surgeon can move the detector inside the patient’s body but the movement is limited. In this study the system was evaluated with an abdomen phantom so that the probe movement was realistic. Moreover, performance of an MLEM algorithm was investigated for the PET-laparoscope system. Experiments with the abdomen phantom confirmed that the system performs well when the probe movement is similar to that achievable during a laparoscopic surgery. MLEM algorithm provided better images. Image artifacts were reduced and the spatial resolution in lateral directions were better …

Positron emission tomography is used to diagnose gastric cancer and identify lymph node metastasis preoperatively. However, locating these lymph node metastases during surgery is difficult and usually leads to unnecessary node removal and results in a deterioration of a patient’s quality of life. To improve the quality of life, intraoperative identification of lymph node metastasis is necessary. A positron emission tomography system consisting of an external detector array connected to the patient’s surgical bed and a movable detector probe which is inserted into the patient’s stomach along with a laparoscopic instrument has been proposed to identify lymph node metastasis intraoperatively. This study presents the image reconstruction of a system involving data acquisition using a movable detector. A simulation-based investigation on the refinement of the detector design is also presented. The image is …

This paper presents our approach in building an image guided navigational tool for neurosurgeons. The paper emphasizes critical methods which we have implemented within the main modules of the system, namely physical space model, calibrated frame and model transformation, MRI space model and the GUI. The surgeon is provided with special tools which contain markers which are detected by cameras to compute the real time positions of the tool tip. Special plastic markers are attached to the patient's head for the registration between two sets of coordinate systems. We were able to obtain satisfactory accuracy when equipped with low quality web cameras. Our GUI provides a modeled three-dimensional viewing of the patients head with the capability of rotating around fixed axes.

審査委員会委員:(主査) 東京大学教授 佐久間 一郎, 東京大学教授 上坂 充, 東京大学教授 瀬戸 泰之, 東京大学教授 高橋 浩之, 東京大学准教授 関野 正樹, 東京大学准教授 原田 香奈子

Prototype PET-laparoscope system for surgical navigation in cancer surgery | CiNii Research CiNii 国立情報学研究所 学術情報ナビゲータ[サイニィ] 詳細へ移動 検索フォームへ移動 論文・データを さがす 大学図書館の本をさがす English 検索 タイトル 人物/団体名 著者ID/研究者番号 所属機関 ISSN DOI 期間 ~ 本文リンク 本文リンクあり データソース JaLC IRDB Crossref DataCite NDLサーチ NDLデジコレ(旧NII-ELS) RUDA JDCat NINJAL CiNii Articles CiNii Books NACSIS-CAT/ILL DBpedia KAKEN Integbio PubMed LSDB Archive 極地研ADS 極地研学術DB OpenAIRE 公共データ カタログ すべて 研究データ 論文 本 博士論文 プロジェクト 人物 > 人物検索機能について Prototype PET-laparoscope system for surgical navigation in cancer surgery NDLデジタルコレクション Madhushanka Rukshani Liyanaarachchi 書誌事項 タイトル Prototype PET-laparoscope system for surgical navigation in cancer surgery タイトル別名 がん手術ナビゲーションのため…